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Updated: 2017-04-23T02:08:11.592-07:00

 



PhotoScan: Taking Glare-Free Pictures of Pictures

2017-04-20T10:42:50.233-07:00

Posted by Ce Liu, Michael Rubinstein, Mike Krainin and Bill Freeman, Research ScientistsYesterday, we released an update to PhotoScan, an app for iOS and Android that allows you to digitize photo prints with just a smartphone. One of the key features of PhotoScan is the ability to remove glare from prints, which are often glossy and reflective, as are the plastic album pages or glass-covered picture frames that host them. To create this feature, we developed a unique blend of computer vision and image processing techniques that can carefully align and combine several slightly different pictures of a print to separate the glare from the image underneath.Left: A regular digital picture of a physical print. Right: Glare-free digital output from PhotoScanWhen taking a single picture of a photo, determining which regions of the picture are the actual photo and which regions are glare is challenging to do automatically. Moreover, the glare may often saturate regions in the picture, rendering it impossible to see or recover the parts of the photo underneath it. But if we take several pictures of the photo while moving the camera, the position of the glare tends to change, covering different regions of the photo. In most cases we found that every pixel of the photo is likely not to be covered by glare in at least one of the pictures. While no single view may be glare-free, we can combine multiple pictures of the printed photo taken at different angles to remove the glare. The challenge is that the images need to be aligned very accurately in order to combine them properly, and this processing needs to run very quickly on the phone to provide a near instant experience. Left: The captured, input images (5 in total). Right: If we stabilize the images on the photo, we can see just the glare moving, covering different parts of the photo. Notice no single image is glare-free.Our technique is inspired by our earlier work published at SIGGRAPH 2015, which we dubbed “obstruction-free photography”. It uses similar principles to remove various types of obstructions from the field of view. However, the algorithm we originally proposed was based on a generative model where the motion and appearance of both the main scene and the obstruction layer are estimated. While that model is quite powerful and can remove a variety of obstructions, it is too computationally expensive to be run on smartphones. We therefore developed a simpler model that treats glare as an outlier, and only attempts to register the underlying, glare-free photo. While this model is simpler, the task is still quite challenging as the registration needs to be highly accurate and robust.How it WorksWe start from a series of pictures of the print taken by the user while moving the camera. The first picture - the “reference frame” - defines the desired output viewpoint. The user is then instructed to take four additional frames. In each additional frame, we detect sparse feature points (we compute ORB features on Harris corners) and use them to establish homographies mapping each frame to the reference frame.Left: Detected feature matches between the reference frame and each other frame (left), and the warped frames according to the estimated homographies (right).While the technique may sound straightforward, there is a catch - homographies are only able to align flat images. But printed photos are often not entirely flat (as is the case with the example shown above). Therefore, we use optical flow — a fundamental, computer vision representation for motion, which establishes pixel-wise mapping between two images — to correct the non-planarities. We start from the homography-aligned frames, and compute “flow fields” to warp the images and further refine the registration. In the example below, notice how the corners of the photo on the left slightly “move” after registering the frames using only homographies. The right hand side shows how the photo is better aligned after refining the registration using optical flow.Comparison between the warped frames usi[...]



Teaching Machines to Draw

2017-04-13T16:37:04.126-07:00

Posted by David Ha, Google Brain ResidentAbstract visual communication is a key part of how people convey ideas to one another. From a young age, children develop the ability to depict objects, and arguably even emotions, with only a few pen strokes. These simple drawings may not resemble reality as captured by a photograph, but they do tell us something about how people represent and reconstruct images of the world around them.Vector drawings produced by sketch-rnn.In our recent paper, “A Neural Representation of Sketch Drawings”, we present a generative recurrent neural network capable of producing sketches of common objects, with the goal of training a machine to draw and generalize abstract concepts in a manner similar to humans. We train our model on a dataset of hand-drawn sketches, each represented as a sequence of motor actions controlling a pen: which direction to move, when to lift the pen up, and when to stop drawing. In doing so, we created a model that potentially has many applications, from assisting the creative process of an artist, to helping teach students how to draw.While there is a already a large body of existing work on generative modelling of images using neural networks, most of the work focuses on modelling raster images represented as a 2D grid of pixels. While these models are currently able to generate realistic images, due to the high dimensionality of a 2D grid of pixels, a key challenge for them is to generate images with coherent structure. For example, these models sometimes produce amusing images of cats with three or more eyes, or dogs with multiple heads.Examples of animals generated with the wrong number of body parts, produced using previous GAN models trained on 128x128 ImageNet dataset. The image above is Figure 29 ofGenerative Adversarial Networks, Ian Goodfellow, NIPS 2016 Tutorial.In this work, we investigate a lower-dimensional vector-based representation inspired by how people draw. Our model, sketch-rnn, is based on the sequence-to-sequence (seq2seq) autoencoder framework. It incorporates variational inference and utilizes hypernetworks as recurrent neural network cells. The goal of a seq2seq autoencoder is to train a network to encode an input sequence into a vector of floating point numbers, called a latent vector, and from this latent vector reconstruct an output sequence using a decoder that replicates the input sequence as closely as possible.Schematic of sketch-rnn.In our model, we deliberately add noise to the latent vector. In our paper, we show that by inducing noise into the communication channel between the encoder and the decoder, the model is no longer be able to reproduce the input sketch exactly, but instead must learn to capture the essence of the sketch as a noisy latent vector. Our decoder takes this latent vector and produces a sequence of motor actions used to construct a new sketch. In the figure below, we feed several actual sketches of cats into the encoder to produce reconstructed sketches using the decoder.Reconstructions from a model trained on cat sketches.It is important to emphasize that the reconstructed cat sketches are not copies of the input sketches, but are instead new sketches of cats with similar characteristics as the inputs. To demonstrate that the model is not simply copying from the input sequence, and that it actually learned something about the way people draw cats, we can try to feed in non-standard sketches into the encoder:When we feed in a sketch of a three-eyed cat, the model generates a similar looking cat that has two eyes instead, suggesting that our model has learned that cats usually only have two eyes. To show that our model is not simply choosing the closest normal-looking cat from a large collection of memorized cat-sketches, we can try to input something totally different, like a sketch of a toothbrush. We see that the network generates a cat-like figure with long whiskers that mimics the features and orientation of the toothbrush. This suggests that the network has learned to encode an [...]



Introducing tf-seq2seq: An Open Source Sequence-to-Sequence Framework in TensorFlow

2017-04-11T13:12:30.769-07:00

Posted by Anna Goldie and Denny Britz, Research Software Engineer and Google Brain Resident, Google Brain Team(Crossposted on the Google Open Source Blog)Last year, we announced Google Neural Machine Translation (GNMT), a sequence-to-sequence (“seq2seq”) model which is now used in Google Translate production systems. While GNMT achieved huge improvements in translation quality, its impact was limited by the fact that the framework for training these models was unavailable to external researchers.Today, we are excited to introduce tf-seq2seq, an open source seq2seq framework in TensorFlow that makes it easy to experiment with seq2seq models and achieve state-of-the-art results. To that end, we made the tf-seq2seq codebase clean and modular, maintaining full test coverage and documenting all of its functionality.Our framework supports various configurations of the standard seq2seq model, such as depth of the encoder/decoder, attention mechanism, RNN cell type, or beam size. This versatility allowed us to discover optimal hyperparameters and outperform other frameworks, as described in our paper, “Massive Exploration of Neural Machine Translation Architectures.”A seq2seq model translating from Mandarin to English. At each time step, the encoder takes in one Chinese character and its own previous state (black arrow), and produces an output vector (blue arrow). The decoder then generates an English translation word-by-word, at each time step taking in the last word, the previous state, and a weighted combination of all the outputs of the encoder (aka attention [3], depicted in blue) and then producing the next English word. Please note that in our implementation we use wordpieces [4] to handle rare words.In addition to machine translation, tf-seq2seq can also be applied to any other sequence-to-sequence task (i.e. learning to produce an output sequence given an input sequence), including machine summarization, image captioning, speech recognition, and conversational modeling. We carefully designed our framework to maintain this level of generality and provide tutorials, preprocessed data, and other utilities for machine translation.We hope that you will use tf-seq2seq to accelerate (or kick off) your own deep learning research. We also welcome your contributions to our GitHub repository, where we have a variety of open issues that we would love to have your help with!Acknowledgments:We’d like to thank Eugene Brevdo, Melody Guan, Lukasz Kaiser, Quoc V. Le, Thang Luong, and Chris Olah for all their help. For a deeper dive into how seq2seq models work, please see the resources below. References:[1] Massive Exploration of Neural Machine Translation Architectures, Denny Britz, Anna Goldie, Minh-Thang Luong, Quoc Le[2] Sequence to Sequence Learning with Neural Networks, Ilya Sutskever, Oriol Vinyals, Quoc V. Le. NIPS, 2014[3] Neural Machine Translation by Jointly Learning to Align and Translate, Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio. ICLR, 2015[4] Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean. Technical Report, 2016[5] Attention and Augmented Recurrent Neural Networks, Chris Olah, Shan Carter. Distill, 2016[6] Neural Machine Translation and Sequence-to-sequence Models: A Tutorial, Graham Neubig[7] Sequence-to-Sequence Models, TensorFlow.org [...]



Announcing the 2017 Google PhD Fellows for North America, Europe and the Middle East

2017-04-10T10:00:23.006-07:00



Google created the PhD Fellowship program in 2009 to recognize and support outstanding graduate students doing exceptional research in Computer Science and related disciplines. Now in its eighth year, our fellowship program has supported hundreds of future faculty, industry researchers, innovators and entrepreneurs.

Reflecting our continuing commitment to supporting and building relationships with the academic community, we are excited to announce the 33 recipients from North America, Europe and the Middle East. We offer our sincere congratulations to Google’s 2017 Class of Google PhD Fellows.

Algorithms, Optimizations and Markets
Chiu Wai Sam Wong, University of California, Berkeley
Eric Balkanski, Harvard University
Haifeng Xu, University of Southern California

Human-Computer Interaction
Motahhare Eslami, University of Illinois, Urbana-Champaign
Sarah D'Angelo, Northwestern University
Sarah Mcroberts, University of Minnesota - Twin Cities

Machine Learning
Aude Genevay, Fondation Sciences Mathématiques de Paris
Dustin Tran, Columbia University
Jamie Hayes, University College London
Martin Arjovsky, New York University
Taco Cohen, University of Amsterdam
Yuhuai Wu, University of Toronto
Yunye Gong, Cornell University

Machine Perception, Speech Technology and Computer Vision
Franziska Müller, Saarland University - Saarbrücken GSCS and MPI Institute for Informatics
George Trigeorgis, Imperial College London
Iro Armeni, Stanford University
Saining Xie, University of California, San Diego
Yu-Chuan Su, University of Texas, Austin

Natural Language Processing
Jianpeng Cheng, The University of Edinburgh
Kevin Clark, Stanford University
Tim Rocktaschel, University College London

Privacy and Security
Romain Gay, ENS - École Normale Supérieure
Xi He, Duke University
Yupeng Zhang, University of Maryland, College Park

Programming Languages and Software Engineering
Christoffer Quist Adamsen, Aarhus University
Muhammad Ali Gulzar, University of California, Los Angeles
Oded Padon, Tel-Aviv University

Structured Data and Database Management
Amir Shaikhha, EPFL CS
Jingbo Shang, University of Illinois, Urbana-Champaign

Systems and Networking
Ahmed M. Said Mohamed Tawfik Issa, Georgia Institute of Technology
Khanh Nguyen, University of California, Irvine
Radhika Mittal, University of California, Berkeley
Ryan Beckett, Princeton University(image)



Predicting Properties of Molecules with Machine Learning

2017-04-07T16:46:16.918-07:00

Posted by George Dahl, Research Scientist, Google Brain TeamRecently there have been many exciting applications of machine learning (ML) to chemistry, particularly in chemical search problems, from drug discovery and battery design to finding better OLEDs and catalysts. Historically, chemists have used numerical approximations to Schrödinger’s equation, such as Density Functional Theory (DFT), in these sorts of chemical searches. However, the computational cost of these approximations limits the size of the search. In the hope of enabling larger searches, several research groups have created ML models to predict chemical properties using training data generated by DFT (e.g. Rupp et al. and Behler and Parrinello). Expanding upon this previous work, we have been applying various modern ML methods to the QM9 benchmark –a public collection of molecules paired with DFT-computed electronic, thermodynamic, and vibrational properties.We have recently posted two papers describing our research in this area that grew out of a collaboration between the Google Brain team, the Google Accelerated Science team, DeepMind, and the University of Basel. The first paper includes a new featurization of molecules and a systematic assessment of a multitude of machine learning methods on the QM9 benchmark. After trying many existing approaches on this benchmark, we worked on improving the most promising deep neural network models. The resulting second paper, “Neural Message Passing for Quantum Chemistry,” describes a model family called Message Passing Neural Networks (MPNNs), which are defined abstractly enough to include many previous neural net models that are invariant to graph symmetries. We developed novel variations within the MPNN family which significantly outperform all baseline methods on the QM9 benchmark, with improvements of nearly a factor of four on some targets. One reason molecular data is so interesting from a machine learning standpoint is that one natural representation of a molecule is as a graph with atoms as nodes and bonds as edges. Models that can leverage inherent symmetries in data will tend to generalize better — part of the success of convolutional neural networks on images is due to their ability to incorporate our prior knowledge about the invariances of image data (e.g. a picture of a dog shifted to the left is still a picture of a dog). Invariance to graph symmetries is a particularly desirable property for machine learning models that operate on graph data, and there has been a lot of interesting research in this area as well (e.g. Li et al., Duvenaud et al., Kearnes et al., Defferrard et al.). However, despite this progress, much work remains. We would like to find the best versions of these models for chemistry (and other) applications and map out the connections between different models proposed in the literature.Our MPNNs set a new state of the art for predicting all 13 chemical properties in QM9. On this particular set of molecules, our model can predict 11 of these properties accurately enough to potentially be useful to chemists, but up to 300,000 times faster than it would take to simulate them using DFT. However, much work remains to be done before MPNNs can be of real practical use to chemists. In particular, MPNNs must be applied to a significantly more diverse set of molecules (e.g. larger or with a more varied set of heavy atoms) than exist in QM9. Of course, even with a realistic training set, generalization to very different molecules could still be poor. Overcoming both of these challenges will involve making progress on questions–such as generalization–that are at the heart of machine learning research.Predicting the properties of molecules is a practically important problem that both benefits from advanced machine learning techniques and presents interesting fundamental research challenges for learning algorithms. Eventually, such predictions could aid the design of new [...]



Federated Learning: Collaborative Machine Learning without Centralized Training Data

2017-04-07T17:03:30.376-07:00

Posted by Brendan McMahan and Daniel Ramage, Research ScientistsStandard machine learning approaches require centralizing the training data on one machine or in a datacenter. And Google has built one of the most secure and robust cloud infrastructures for processing this data to make our services better. Now for models trained from user interaction with mobile devices, we're introducing an additional approach: Federated Learning. Federated Learning enables mobile phones to collaboratively learn a shared prediction model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. This goes beyond the use of local models that make predictions on mobile devices (like the Mobile Vision API and On-Device Smart Reply) by bringing model training to the device as well.It works like this: your device downloads the current model, improves it by learning from data on your phone, and then summarizes the changes as a small focused update. Only this update to the model is sent to the cloud, using encrypted communication, where it is immediately averaged with other user updates to improve the shared model. All the training data remains on your device, and no individual updates are stored in the cloud. Your phone personalizes the model locally, based on your usage (A). Many users' updates are aggregated (B) to form a consensus change (C) to the shared model, after which the procedure is repeated.Federated Learning allows for smarter models, lower latency, and less power consumption, all while ensuring privacy. And this approach has another immediate benefit: in addition to providing an update to the shared model, the improved model on your phone can also be used immediately, powering experiences personalized by the way you use your phone.We're currently testing Federated Learning in Gboard on Android, the Google Keyboard. When Gboard shows a suggested query, your phone locally stores information about the current context and whether you clicked the suggestion. Federated Learning processes that history on-device to suggest improvements to the next iteration of Gboard’s query suggestion model.To make Federated Learning possible, we had to overcome many algorithmic and technical challenges. In a typical machine learning system, an optimization algorithm like Stochastic Gradient Descent (SGD) runs on a large dataset partitioned homogeneously across servers in the cloud. Such highly iterative algorithms require low-latency, high-throughput connections to the training data. But in the Federated Learning setting, the data is distributed across millions of devices in a highly uneven fashion. In addition, these devices have significantly higher-latency, lower-throughput connections and are only intermittently available for training.These bandwidth and latency limitations motivate our Federated Averaging algorithm, which can train deep networks using 10-100x less communication compared to a naively federated version of SGD. The key idea is to use the powerful processors in modern mobile devices to compute higher quality updates than simple gradient steps. Since it takes fewer iterations of high-quality updates to produce a good model, training can use much less communication. As upload speeds are typically much slower than download speeds, we also developed a novel way to reduce upload communication costs up to another 100x by compressing updates using random rotations and quantization. While these approaches are focused on training deep networks, we've also designed algorithms for high-dimensional sparse convex models which excel on problems like click-through-rate prediction.Deploying this technology to millions of heterogenous phones running Gboard requires a sophisticated technology stack. On device training uses a miniature version of TensorFlow. Careful scheduling ensures training happens only when the device is idle, plugged [...]



Keeping fake listings off Google Maps

2017-04-06T09:33:16.507-07:00

Posted by Doug Grundman, Maps Anti-Abuse, and Kurt Thomas, Security & Anti-Abuse Research(Crossposted on the Google Security blog)Google My Business enables millions of business owners to create listings and share information about their business on Google Maps and Search, making sure everything is up-to-date and accurate for their customers. Unfortunately, some actors attempt to abuse this service to register fake listings in order to defraud legitimate business owners, or to charge exorbitant service fees for services.Over a year ago, we teamed up with the University of California, San Diego to research the actors behind fake listings, in order to improve our products and keep our users safe. The full report, “Pinning Down Abuse on Google Maps”, will be presented tomorrow at the 2017 International World Wide Web Conference.Our study shows that fewer than 0.5% of local searches lead to fake listings. We’ve also improved how we verify new businesses, which has reduced the number of fake listings by 70% from its all-time peak back in June 2015.What is a fake listing?For over a year, we tracked the bad actors behind fake listings. Unlike email-based scams selling knock-off products online, local listing scams require physical proximity to potential victims. This fundamentally changes both the scale and types of abuse possible.Bad actors posing as locksmiths, plumbers, electricians, and other contractors were the most common source of abuse—roughly 2 out of 5 fake listings. The actors operating these fake listings would cycle through non-existent postal addresses and disposable VoIP phone numbers even as their listings were discovered and disabled. The purported addresses for these businesses were irrelevant as the contractors would travel directly to potential victims.Another 1 in 10 fake listings belonged to real businesses that bad actors had improperly claimed ownership over, such as hotels and restaurants. While making a reservation or ordering a meal was indistinguishable from the real thing, behind the scenes, the bad actors would deceive the actual business into paying referral fees for organic interest.How does Google My Business verify information?Google My Business currently verifies the information provided by business owners before making it available to users. For freshly created listings, we physically mail a postcard to the new listings’ address to ensure the location really exists. For businesses changing owners, we make an automated call to the listing’s phone number to verify the change.Unfortunately, our research showed that these processes can be abused to get fake listings on Google Maps. Fake contractors would request hundreds of postcard verifications to non-existent suites at a single address, such as 123 Main St #456 and 123 Main St #789, or to stores that provided PO boxes. Alternatively, a phishing attack could maliciously repurpose freshly verified business listings by tricking the legitimate owner into sharing verification information sent either by phone or postcard.Keeping deceptive businesses out — by the numbersLeveraging our study’s findings, we’ve made significant changes to how we verify addresses and are even piloting an advanced verification process for locksmiths and plumbers. Improvements we’ve made include prohibiting bulk registrations at most addresses, preventing businesses from relocating impossibly far from their original address without additional verification, and detecting and ignoring intentionally mangled text in address fields designed to confuse our algorithms. We have also adapted our anti-spam machine learning systems to detect data discrepancies common to fake or deceptive listings.Combined, here’s how these defenses stack up:We detect and disable 85% of fake listings before they even appear on Google Maps.We’ve reduced the number of abusive listings by 70% from its peak back i[...]



And the award goes to...

2017-04-05T01:00:17.184-07:00

Posted by Evgeniy Gabrilovich, Senior Staff Research Scientist, Google Research, and WWW 2017 Technical Program Co-ChairToday, Google's Andrei Broder, Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, and Andrew Tomkins, along with their coauthors, Farzin Maghoul, Raymie Stata, and Janet Wiener, have received the prestigious 2017 Seoul Test of Time Award for their classic paper “Graph Structure in the Web”. This award is given to the authors of a previous World Wide Web conference paper that has demonstrated significant scientific, technical, or social impact over the years. The first award, introduced in 2015, was given to Google founders Larry Page and Sergey Brin. Originally presented in 2000 at the 9th WWW conference in Amsterdam, “Graph Structure in the Web” represents the seminal study of the structure of the World Wide Web. At the time of publication, it received the Best Paper Award from the WWW conference, and in the following 17 years proved to be highly influential, accumulating over 3,500 citations. The paper made two major contributions to the study of the structure of the Internet. First, it reported the results of a very large scale experiment to confirm that the indegree of Web nodes is distributed according to a power law. To wit, the probability that a node of the Web graph has i incoming links is roughly proportional to 1/i2.1. Second, in contrast to previous research that assumed the Web to be almost fully connected, “Graph Structure in the Web” described a much more elaborate structure of the Web, which since then has been depicted with the iconic “bowtie” shape:Original “bowtie” schematic from “Graph Structure in the Web”The authors presented a refined model of the Web graph, and described several characteristic classes of Web pages:the strongly connected core component, where each page is reachable from any other page,the so-called IN and OUT clusters, which only have unidirectional paths to or from the core,tendrils dangling from the two clusters, and tubes connecting the clusters while bypassing the core, and finallydisconnected components, which are isolated from the rest of the graph.Whereas the core component is fully connected and each node can be reached from any other node, Broder et al. discovered that as a whole the Web is much more loosely connected than previously believed, while the probability that any two given pages can be reached from one another is just under 1/4.Ravi Kumar, presenting the original paper in Amsterdam at WWW 2000Curiously, the original study was done back in 1999 on two Altavista crawls having 200 million pages and 1.5 billion links. Today, Google indexes over 100 billion links merely within apps, and overall processes over 130 trillion web addresses in its web crawls.Over the years, the power law was found to be characteristic of many other Web-related phenomena, including the structure of social networks and the distribution of search query frequencies. The description of the macroscopic structure of the Web graph proposed by Broder et al. provided a solid mathematical foundation for numerous subsequent studies on crawling and searching the Web, which profoundly influenced the architecture of modern search engines.Hearty congratulations to all the authors on the well-deserved award! [...]



Consistent Hashing with Bounded Loads

2017-04-03T10:00:23.496-07:00

Posted by Vahab Mirrokni, Principal Scientist, Morteza Zadimoghaddam, Research Scientist, NYC Algorithms TeamRunning a large-scale web service, such as content hosting, necessarily requires load balancing — distributing clients uniformly across multiple servers such that none get overloaded. Further, it is desirable to find an allocation that does not change very much over time in a dynamic environment in which both clients and servers can be added or removed at any time. In other words, we need the allocation of clients to servers to be consistent over time.In collaboration with Mikkel Thorup, a visiting researcher from university of Copenhagen, we developed a new efficient allocation algorithm for this problem with tight guarantees on the maximum load of each server, and studied it theoretically and empirically. We then worked with our Cloud team to implement it in Google Cloud Pub/Sub, a scalable event streaming service, and observed substantial improvement on uniformity of the load allocation (in terms of the maximum load assigned to servers) while maintaining consistency and stability objectives. In August 2016 we described our algorithm in the paper “Consistent Hashing with Bounded Loads”, and shared it on ArXiv for potential use by the broader research community. Three months later, Andrew Rodland from Vimeo informed us that he had found the paper, implemented it in haproxy (a widely-used piece of open source software), and used it for their load balancing project at Vimeo. The results were dramatic: applying these algorithmic ideas helped them decrease the cache bandwidth by a factor of almost 8, eliminating a scaling bottleneck. He recently summarized this story in a blog post detailing his use case. Needless to say, we were excited to learn that our theoretical research was not only put into application, but also that it was useful and open-sourced. BackgroundWhile the concept of consistent hashing has been developed in the past to deal with load balancing in dynamic environments, a fundamental issue with all the previously developed schemes is that, in certain scenarios, they may result in sub-optimal load balancing on many servers. Additionally, both clients and servers may be added or removed periodically, and with such changes, we do not want to move too many clients. Thus, while the dynamic allocation algorithm has to always ensure a proper load balancing, it should also aim to minimize the number of clients moved after each change to the system. Such allocation problems become even more challenging when we face hard constraints on the capacity of each server - that is, each server has a capacity that the load may not exceed. Typically, we want capacities close to the average loads. In other words, we want to simultaneously achieve both uniformity and consistency in the resulting allocations. There is a vast amount of literature on solutions in the much simpler case where the set of servers is fixed and only the client set is updated, but in this post we discuss solutions that are relevant in the fully dynamic case where both clients and servers can be added and removed. The AlgorithmWe can think about the servers as bins and clients as balls to have a similar notation with well-studied balls-to-bins stochastic processes. The uniformity objective encourages all bins to have a load roughly equal to the average density (the number of balls divided by the number of bins). For some parameter ε, we set the capacity of each bin to either floor or ceiling of the average load times (1+ε). This extra capacity allows us to design an allocation algorithm that meets the consistency objective in addition to the uniformity property. Imagine a given range of numbers overlaid on a circle. We apply a hash function to balls and a separate hash function to bins to obtain numbers in that range that corr[...]



Announcing AudioSet: A Dataset for Audio Event Research

2017-03-30T10:12:42.455-07:00

Posted by Dan Ellis, Research Scientist, Sound Understanding TeamSystems able to recognize sounds familiar to human listeners have a wide range of applications, from adding sound effect information to automatic video captions, to potentially allowing you to search videos for specific audio events. Building Deep Learning systems to do this relies heavily on both a large quantity of computing (often from highly parallel GPUs), and also – and perhaps more importantly – on significant amounts of accurately-labeled training data. However, research in environmental sound recognition is limited by currently available public datasets.In order to address this, we recently released AudioSet, a collection of over 2 million ten-second YouTube excerpts labeled with a vocabulary of 527 sound event categories, with at least 100 examples for each category. Announced in our paper at the IEEE International Conference on Acoustics, Speech, and Signal Processing, AudioSet provides a common, realistic-scale evaluation task for audio event detection and a starting point for a comprehensive vocabulary of sound events, designed to advance research into audio event detection and recognition. Developing an OntologyWhen we started on this work last year, our first task was to define a vocabulary of sound classes that provided a consistent level of detail over the spectrum of sound events we planned to label. Defining this ontology was necessary to avoid problems of ambiguity and synonyms; without this, we might end up trying to differentiate “Timpani” from “Kettle drum”, or “Water tap” from “Faucet”. Although a number of scientists have looked at how humans organize sound events, the few existing ontologies proposed have been small and partial. To build our own, we searched the web for phrases like “Sounds, such as X and Y”, or “X, Y, and other sounds”. This gave us a list of sound-related words which we manually sorted into a hierarchy of over 600 sound event classes ranging from “Child speech” to “Ukulele” to “Boing”. To make our taxonomy as comprehensive as possible, we then looked at comparable lists of sound events (for instance, the Urban Sound Taxonomy) to add significant classes we may have missed and to merge classes that weren't well defined or well distinguished. You can explore our ontology here.The top two levels of the AudioSet ontology.From Ontology to Labeled DataWith our new ontology in hand, we were able to begin collecting human judgments of where the sound events occur. This, too, raises subtle problems: unlike the billions of well-composed photographs available online, people don’t typically produce “well-framed” sound recordings, much less provide them with captions. We decided to use 10 second sound snippets as our unit; anything shorter becomes very difficult to identify in isolation. We collected candidate snippets for each of our classes by taking random excerpts from YouTube videos whose metadata indicated they might contain the sound in question (“Dogs Barking for 10 Hours”). Each snippet was presented to a human labeler with a small set of category names to be confirmed (“Do you hear a Bark?”). Subsequently, we proposed snippets whose content was similar to examples that had already been manually verified to contain the class, thereby finding examples that were not discoverable from the metadata. Because some classes were much harder to find than others – particularly the onomatopoeia words like “Squish” and “Clink” – we adapted our segment proposal process to increase the sampling for those categories. For more details, see our paper on the matching technique. AudioSet provides the URLs of each video excerpt along with the sound classes that the raters confirmed as present, as well as precalculated audio[...]



Adding Sound Effect Information to YouTube Captions

2017-03-23T10:03:08.836-07:00

Posted by Sourish Chaudhuri, Software Engineer, Sound UnderstandingThe effect of audio on our perception of the world can hardly be overstated. Its importance as a communication medium via speech is obviously the most familiar, but there is also significant information conveyed by ambient sounds. These ambient sounds create context that we instinctively respond to, like getting startled by sudden commotion, the use of music as a narrative element, or how laughter is used as an audience cue in sitcoms. Since 2009, YouTube has provided automatic caption tracks for videos, focusing heavily on speech transcription in order to make the content hosted more accessible. However, without similar descriptions of the ambient sounds in videos, much of the information and impact of a video is not captured by speech transcription alone. To address this, we announced the addition of sound effect information to the automatic caption track in YouTube videos, enabling greater access to the richness of all the audio content.In this post, we discuss the backend system developed for this effort, a collaboration among the Accessibility, Sound Understanding and YouTube teams that used machine learning (ML) to enable the first ever automatic sound effect captioning system for YouTube. allowfullscreen="" class="YOUTUBE-iframe-video" data-thumbnail-src="https://i.ytimg.com/vi/QGiK8DAZ9BA/0.jpg" frameborder="0" height="360" src="https://www.youtube.com/embed/QGiK8DAZ9BA?rel=0&start=15&end=55;feature=player_embedded" width="640">Click the CC button to see the sound effect captioning system in action.The application of ML – in this case, a Deep Neural Network (DNN) model – to the captioning task presented unique challenges. While the process of analyzing the time-domain audio signal of a video to detect various ambient sounds is similar to other well known classification problems (such as object detection in images), in a product setting the solution faces additional difficulties. In particular, given an arbitrary segment of audio, we need our models to be able to 1) detect the desired sounds, 2) temporally localize the sound in the segment and 3) effectively integrate it in the caption track, which may have parallel and independent speech recognition results.A DNN Model for Ambient SoundThe first challenge we faced in developing the model was the task of obtaining enough labeled data suitable for training our neural network. While labeled ambient sound information is difficult to come by, we were able to generate a large enough dataset for training using weakly labeled data. But of all the ambient sounds in a given video, which ones should we train our DNN to detect? For the initial launch of this feature, we chose [APPLAUSE], [MUSIC] and [LAUGHTER], prioritized based upon our analysis of human-created caption tracks that indicates that they are among the most frequent sounds that are manually captioned. While the sound space is obviously far richer and provides even more contextually relevant information than these three classes, the semantic information conveyed by these sound effects in the caption track is relatively unambiguous, as opposed to sounds like [RING] which raises the question of “what was it that rang – a bell, an alarm, a phone?”Much of our initial work on detecting these ambient sounds also included developing the infrastructure and analysis frameworks to enable scaling for future work, including both the detection of sound events and their integration into the automatic caption track. Investing in the development of this infrastructure has the added benefit of allowing us to easily incorporate more sound types in the future, as we expand our algorithms to understand a wider vocabulary of sounds (e.g. [RING], [KNOCK], [BARK]). In doing so[...]



Distill: Supporting Clarity in Machine Learning

2017-03-20T15:00:37.566-07:00

Posted by Shan Carter, Software Engineer and Chris Olah, Research Scientist, Google Brain TeamScience isn't just about discovering new results. It’s also about human understanding. Scientists need to develop notations, analogies, visualizations, and explanations of ideas. This human dimension of science isn't a minor side project. It's deeply tied to the heart of science.That’s why, in collaboration with OpenAI, DeepMind, YC Research, and others, we’re excited to announce the launch of Distill, a new open science journal and ecosystem supporting human understanding of machine learning. Distill is an independent organization, dedicated to fostering a new segment of the research community.Modern web technology gives us powerful new tools for expressing this human dimension of science. We can create interactive diagrams and user interfaces the enable intuitive exploration of research ideas. Over the last few years we've seen many incredible demonstrations of this kind of work.An interactive diagram explaining the Neural Turing Machine from Olah & Carter, 2016.Unfortunately, while there are a plethora of conferences and journals in machine learning, there aren’t any research venues that are dedicated to publishing this kind of work. This is partly an issue of focus, and partly because traditional publication venues can't, by virtue of their medium, support interactive visualizations. Without a venue to publish in, many significant contributions don’t count as “real academic contributions” and their authors can’t access the academic support structure.That’s why Distill aims to build an ecosystem to support this kind of work, starting with three pieces: a research journal, prizes recognizing outstanding work, and tools to facilitate the creation of interactive articles.Distill is an ecosystem to support clarity in Machine Learning.Led by a diverse steering committee of leaders from the machine learning and user interface communities, we are very excited to see where Distill will go. To learn more about Distill, see the overview page or read the latest articles. [...]



Announcing Guetzli: A New Open Source JPEG Encoder

2017-03-17T13:59:08.939-07:00

Posted by Robert Obryk and Jyrki Alakuijala, Software Engineers, Google Research Europe(Cross-posted on the Google Open Source Blog)At Google, we care about giving users the best possible online experience, both through our own services and products and by contributing new tools and industry standards for use by the online community. That’s why we’re excited to announce Guetzli, a new open source algorithm that creates high quality JPEG images with file sizes 35% smaller than currently available methods, enabling webmasters to create webpages that can load faster and use even less data.Guetzli [guɛtsli] — cookie in Swiss German — is a JPEG encoder for digital images and web graphics that can enable faster online experiences by producing smaller JPEG files while still maintaining compatibility with existing browsers, image processing applications and the JPEG standard. From the practical viewpoint this is very similar to our Zopfli algorithm, which produces smaller PNG and gzip files without needing to introduce a new format, and different than the techniques used in RNN-based image compression, RAISR, and WebP, which all need client changes for compression gains at internet scale. The visual quality of JPEG images is directly correlated to its multi-stage compression process: color space transform, discrete cosine transform, and quantization. Guetzli specifically targets the quantization stage in which the more visual quality loss is introduced, the smaller the resulting file. Guetzli strikes a balance between minimal loss and file size by employing a search algorithm that tries to overcome the difference between the psychovisual modeling of JPEG's format, and Guetzli’s psychovisual model, which approximates color perception and visual masking in a more thorough and detailed way than what is achievable by simpler color transforms and the discrete cosine transform. However, while Guetzli creates smaller image file sizes, the tradeoff is that these search algorithms take significantly longer to create compressed images than currently available methods.Figure 1. 16x16 pixel synthetic example of a phone line hanging against a blue sky — traditionally a case where JPEG compression algorithms suffer from artifacts. Uncompressed original is on the left. Guetzli (on the right) shows less ringing artefacts than libjpeg (middle) and has a smaller file size.And while Guetzli produces smaller image file sizes without sacrificing quality, we additionally found that in experiments where compressed image file sizes are kept constant that human raters consistently preferred the images Guetzli produced over libjpeg images, even when the libjpeg files were the same size or even slightly larger. We think this makes the slower compression a worthy tradeoff.Figure 2. 20x24 pixel zoomed areas from a picture of a cat’s eye. Uncompressed original on the left. Guetzli (on the right) shows less ringing artefacts than libjpeg (middle) without requiring a larger file size.It is our hope that webmasters and graphic designers will find Guetzli useful and apply it to their photographic content, making users’ experience smoother on image-heavy websites in addition to reducing load times and bandwidth costs for mobile users. Last, we hope that the new explicitly psychovisual approach in Guetzli will inspire further image and video compression research. [...]



An Upgrade to SyntaxNet, New Models and a Parsing Competition

2017-03-16T09:21:53.876-07:00

Posted by David Weiss and Slav Petrov, Research ScientistsAt Google, we continuously improve the language understanding capabilities used in applications ranging from generation of email responses to translation. Last summer, we open-sourced SyntaxNet, a neural-network framework for analyzing and understanding the grammatical structure of sentences. Included in our release was Parsey McParseface, a state-of-the-art model that we had trained for analyzing English, followed quickly by a collection of pre-trained models for 40 additional languages, which we dubbed Parsey's Cousins. While we were excited to share our research and to provide these resources to the broader community, building machine learning systems that work well for languages other than English remains an ongoing challenge. We are excited to announce a few new research resources, available now, that address this problem.SyntaxNet UpgradeWe are releasing a major upgrade to SyntaxNet. This upgrade incorporates nearly a year’s worth of our research on multilingual language understanding, and is available to anyone interested in building systems for processing and understanding text. At the core of the upgrade is a new technology that enables learning of richly layered representations of input sentences. More specifically, the upgrade extends TensorFlow to allow joint modeling of multiple levels of linguistic structure, and to allow neural-network architectures to be created dynamically during processing of a sentence or document.Our upgrade makes it, for example, easy to build character-based models that learn to compose individual characters into words (e.g. ‘c-a-t’ spells ‘cat’). By doing so, the models can learn that words can be related to each other because they share common parts (e.g. ‘cats’ is the plural of ‘cat’ and shares the same stem; ‘wildcat’ is a type of ‘cat’). Parsey and Parsey’s Cousins, on the other hand, operated over sequences of words. As a result, they were forced to memorize words seen during training and relied mostly on the context to determine the grammatical function of previously unseen words. As an example, consider the following (meaningless but grammatically correct) sentence: This sentence was originally coined by Andrew Ingraham who explained: “You do not know what this means; nor do I. But if we assume that it is English, we know that the doshes are distimmed by the gostak. We know too that one distimmer of doshes is a gostak." Systematic patterns in morphology and syntax allow us to guess the grammatical function of words even when they are completely novel: we understand that ‘doshes’ is the plural of the noun ‘dosh’ (similar to the ‘cats’ example above) or that ‘distim’ is the third person singular of the verb distim. Based on this analysis we can then derive the overall structure of this sentence even though we have never seen the words before.ParseySaurusTo showcase the new capabilities provided by our upgrade to SyntaxNet, we are releasing a set of new pretrained models called ParseySaurus. These models use the character-based input representation mentioned above and are thus much better at predicting the meaning of new words based both on their spelling and how they are used in context. The ParseySaurus models are far more accurate than Parsey’s Cousins (reducing errors by as much as 25%), particularly for morphologically-rich languages like Russian, or agglutinative languages like Turkish and Hungarian. In those languages there can be dozens of forms for each word and many of these forms might never be observed during training - even in a very large corpus.Consider the following fictitious Russian sentence, where again the[...]



Quick Access in Drive: Using Machine Learning to Save You Time

2017-03-10T10:09:10.443-08:00

Posted by Sandeep Tata, Software Engineer, Google ResearchAt Google, we research cutting-edge machine learning (ML) techniques that allow us to provide products and services aimed at helping you focus on what’s important. From providing language translations to understanding images to helping you respond to emails, it is our goal to help you save time, making life — and work — a little more convenient.Recent studies have shown that finding information is second only to managing email as a drain on workplace productivity. To help address this, last year we launched Quick Access, a feature in Google Drive that uses ML to surface the most relevant documents as soon as you visit the Google Drive home screen. Originally available only for G Suite customers on Android, Quick Access is now available for anyone who uses Google Drive (on the Web, Android, and iOS), saving you from having to enter a search or to browse through your folders. Our metrics show that Quick Access takes you to the documents you need in half the time compared to manually navigating or searching.Quick Access uses deep neural networks to determine patterns from various signals, such as activity in Drive, meetings on your Calendar, and more, to anticipate your needs and show the appropriate documents on the Drive home screen. Traditional ML approaches require domain experts to derive complex features from data, which are in turn used to train the model. For Quick Access, however, we constructed thousands of simple features from the various signals above (for instance, the timestamps of the last 20 edit events on a document would constitute 20 simple input features), and combined them with the power of deep neural networks to learn from the aggregated activity of our users. By using deep neural networks we were able to develop accurate predictive models with simpler features and less feature engineering effort.Quick Access suggestions on the top row in Drive on a desktop browser.The model computes a relevance score for each of the documents in Drive and the top scoring documents are presented on the home screen. For example, if you have a Calendar entry for a meeting with a coworker in the next few minutes, Quick Access might predict that the presentation you’ve been working on with that coworker is more relevant compared to your monthly budget spreadsheet or the photos you uploaded last week. If you’ve been updating a spreadsheet every weekend, then next weekend, Quick Access will likely display that spreadsheet ahead of the other documents you viewed during the week.We hope Quick Access helps you use Drive more effectively, allowing you to save time and be more productive. To learn more, watch this talk from Google Cloud Next ‘17 that dives into more details on the ML behind Quick Access.AcknowledgementsThanks to Alexandrin Popescul and Marc Najork for contributions that made this application of machine learning technology possible. This work was in close collaboration with several engineers on the Drive team including Sean Abraham, Brian Calaci, Mike Colagrosso, Mike Procopio, Jesse Sterr, and Timothy Vis. [...]



Assisting Pathologists in Detecting Cancer with Deep Learning

2017-03-07T17:48:49.296-08:00

Posted by Martin Stumpe, Technical Lead, and Lily Peng, Product ManagerA pathologist’s report after reviewing a patient’s biological tissue samples is often the gold standard in the diagnosis of many diseases. For cancer in particular, a pathologist’s diagnosis has a profound impact on a patient’s therapy. The reviewing of pathology slides is a very complex task, requiring years of training to gain the expertise and experience to do well.Even with this extensive training, there can be substantial variability in the diagnoses given by different pathologists for the same patient, which can lead to misdiagnoses. For example, agreement in diagnosis for some forms of breast cancer can be as low as 48%, and similarly low for prostate cancer. The lack of agreement is not surprising given the massive amount of information that must be reviewed in order to make an accurate diagnosis. Pathologists are responsible for reviewing all the biological tissues visible on a slide. However, there can be many slides per patient, each of which is 10+ gigapixels when digitized at 40X magnification. Imagine having to go through a thousand 10 megapixel (MP) photos, and having to be responsible for every pixel. Needless to say, this is a lot of data to cover, and often time is limited.To address these issues of limited time and diagnostic variability, we are investigating how deep learning can be applied to digital pathology, by creating an automated detection algorithm that can naturally complement pathologists’ workflow. We used images (graciously provided by the Radboud University Medical Center) which have also been used for the 2016 ISBI Camelyon Challenge1 to train algorithms that were optimized for localization of breast cancer that has spread (metastasized) to lymph nodes adjacent to the breast. The results? Standard “off-the-shelf” deep learning approaches like Inception (aka GoogLeNet) worked reasonably well for both tasks, although the tumor probability prediction heatmaps produced were a bit noisy. After additional customization, including training networks to examine the image at different magnifications (much like what a pathologist does), we showed that it was possible to train a model that either matched or exceeded the performance of a pathologist who had unlimited time to examine the slides.Left: Images from two lymph node biopsies. Middle: earlier results of our deep learning tumor detection. Right: our current results. Notice the visibly reduced noise (potential false positives) between the two versions.In fact, the prediction heatmaps produced by the algorithm had improved so much that the localization score (FROC) for the algorithm reached 89%, which significantly exceeded the score of 73% for a pathologist with no time constraint2. We were not the only ones to see promising results, as other groups were getting scores as high as 81% with the same dataset. Even more exciting for us was that our model generalized very well, even to images that were acquired from a different hospital using different scanners. For full details, see our paper “Detecting Cancer Metastases on Gigapixel Pathology Images”.A closeup of a lymph node biopsy. The tissue contains a breast cancer metastasis as well as macrophages, which look similar to tumor but are benign normal tissue. Our algorithm successfully identifies the tumor region (bright green) and is not confused by the macrophages.While these results are promising, there are a few important caveats to consider.Like most metrics, the FROC localization score is not perfect. Here, the FROC score is defined as the sensitivity (percentage of tumors detected) at a few pre-defi[...]



Google Research Awards 2016

2017-03-14T11:01:30.175-07:00



We’ve just completed another round of the Google Research Awards, our annual open call for proposals on computer science and related topics including machine learning, machine perception, natural language processing, and security. Our grants cover tuition for a graduate student and provide both faculty and students the opportunity to work directly with Google researchers and engineers.

This round we received 876 proposals covering 44 countries and over 300 universities. After expert reviews and committee discussions, we decided to fund 143 projects. Here are a few observations from this round:


Congratulations to the well-deserving recipients of this round’s awards. If you are interested in applying for the next round (deadline is September 30th), please visit our website for more information.(image)



Preprocessing for Machine Learning with tf.Transform

2017-02-22T10:30:37.055-08:00

Posted by Kester Tong, David Soergel, and Gus Katsiapis, Software EngineersWhen applying machine learning to real world datasets, a lot of effort is required to preprocess data into a format suitable for standard machine learning models, such as neural networks. This preprocessing takes a variety of forms, from converting between formats, to tokenizing and stemming text and forming vocabularies, to performing a variety of numerical operations such as normalization.Today we are announcing tf.Transform, a library for TensorFlow that allows users to define preprocessing pipelines and run these using large scale data processing frameworks, while also exporting the pipeline in a way that can be run as part of a TensorFlow graph. Users define a pipeline by composing modular Python functions, which tf.Transform then executes with Apache Beam, a framework for large-scale, efficient, distributed data processing. Apache Beam pipelines can be run on Google Cloud Dataflow with planned support for running with other frameworks. The TensorFlow graph exported by tf.Transform enables the preprocessing steps to be replicated when the trained model is used to make predictions, such as when serving the model with Tensorflow Serving.A common problem encountered when running machine learning models in production is "training-serving skew", where the data seen at serving time differs in some way from the data used to train the model, leading to reduced prediction quality. tf.Transform ensures that no skew can arise during preprocessing, by guaranteeing that the serving-time transformations are exactly the same as those performed at training time, in contrast to when training-time and serving-time preprocessing are implemented separately in two different environments (e.g., Apache Beam and TensorFlow, respectively).In addition to facilitating preprocessing, tf.Transform allows users to compute summary statistics for their datasets. Understanding the data is very important in every machine learning project, as subtle errors can arise from making wrong assumptions about what the underlying data look like. By making the computation of summary statistics easy and efficient, tf.Transform allows users to check their assumptions about both raw and preprocessed data.tf.Transform allows users to define a preprocessing pipeline. Users can materialize the preprocessed data for use in TensorFlow training, and also export a tf.Transform graph that encodes the transformations as a TensorFlow graph. This transformation graph can then be incorporated into the model graph used for inference.We’re excited to be releasing this latest addition to the TensorFlow ecosystem, and we hope users will find it useful for preprocessing and understanding their data.AcknowledgementsWe wish to thank the following members of the tf.Transform team for their contributions to this project: Clemens Mewald, Robert Bradshaw, Rajiv Bharadwaja, Elmer Garduno, Afshin Rostamizadeh, Neoklis Polyzotis, Abhi Rao, Joe Toth, Neda Mirian, Dinesh Kulkarni, Robbie Haertel, Cyril Bortolato and Slaven Bilac. We also wish to thank the TensorFlow, TensorFlow Serving and Cloud Dataflow teams for their support. [...]



Headset “Removal” for Virtual and Mixed Reality

2017-02-22T11:21:47.271-08:00

Posted by Vivek Kwatra, Research Scientist and Christian Frueh, Avneesh Sud, Software EngineersVirtual Reality (VR) enables remarkably immersive experiences, offering new ways to view the world and the ability to explore novel environments, both real and imaginary. However, compared to physical reality, sharing these experiences with others can be difficult, as VR headsets make it challenging to create a complete picture of the people participating in the experience.Some of this disconnect is alleviated by Mixed Reality (MR), a related medium that shares the virtual context of a VR user in a two dimensional video format allowing other viewers to get a feel for the user’s virtual experience. Even though MR facilitates sharing, the headset continues to block facial expressions and eye gaze, presenting a significant hurdle to a fully engaging experience and complete view of the person in VR.Google Machine Perception researchers, in collaboration with Daydream Labs and YouTube Spaces, have been working on solutions to address this problem wherein we reveal the user’s face by virtually “removing” the headset and create a realistic see-through effect.VR user captured in front of a green-screen is blended with the virtual environment to generate the MR output: Traditional MR output has the user face occluded, while our result reveals the face. Note how the headset is modified with a marker to aid tracking.Our approach uses a combination of 3D vision, machine learning and graphics techniques, and is best explained in the context of enhancing Mixed Reality video (also discussed in the Google-VR blog). It consists of three main components:Dynamic face model captureThe core idea behind our technique is to use a 3D model of the user’s face as a proxy for the hidden face. This proxy is used to synthesize the face in the MR video, thereby creating an impression of the headset being removed. First, we capture a personalized 3D face model for the user with what we call gaze-dependent dynamic appearance. This initial calibration step requires the user to sit in front of a color+depth camera and a monitor, and then track a marker on the monitor with their eyes. We use this one-time calibration procedure — which typically takes less than a minute — to acquire a 3D face model of the user, and learn a database that maps appearance images (or textures) to different eye-gaze directions and blinks. This gaze database (i.e. the face model with textures indexed by eye-gaze) allows us to dynamically change the appearance of the face during synthesis and generate any desired eye-gaze, thus making the synthesized face look natural and aliveOn the left, the user’s face is captured by a camera as she tracks a marker on the monitor with her eyes. On the right, we show the dynamic nature of reconstructed 3D face model: by moving or clicking on the mouse, we are able to simulate both apparent eye gaze and blinking.Calibration and AlignmentCreating a Mixed Reality video requires a specialized setup consisting of an external camera, calibrated and time-synced with the headset. The camera captures a video stream of the VR user in front of a green screen and then composites a cutout of the user with the virtual world to create the final MR video. An important step here is to accurately estimate the calibration (the fixed 3D transformation) between the camera and headset coordinate systems. These calibration techniques typically involve significant manual intervention and are done in multiple steps. We simplify the process by adding a physical marker to the f[...]



The CS Capacity Program - New Tools and SIGCSE 2017

2017-02-16T11:04:41.953-08:00

Posted by Chris Stephenson, Head of Computer Science Education StrategyThe CS Capacity program was launched in March of 2015 to help address a dramatic increase in undergraduate computer science enrollments that is creating serious resource and pedagogical challenges for many colleges and universities. Over the last two years, a diverse group of universities have been working to develop successful strategies that support the expansion of high-quality CS programs at the undergraduate level. Their work focuses on innovations in teaching and technologies that support scaling while ensuring the engagement of women and underrepresented students. These innovations could provide assistance to many other institutions that are challenged to provide a high-quality educational experience to an increasing number of introductory-level students.The cohort of CS Capacity institutions include George Mason University, Mount Holyoke College, Rutgers University, and the University California Berkeley which are working individually, and Duke University, North Carolina State University, the University of Florida, and the University of North Carolina which are working together. These institution each brings a unique approach to addressing CS capacity challenges. Two years into the program, we're sharing an update on some of the great projects and ideas to emerge so far. At George Mason, for example, computer science professor Jeff Offutt and his team have developed an online system to provide self-paced learning for CS1 and CS2 classes that allows learners through the learning materials wore quickly or slowly depending on their needs. The system, called SPARC, includes course content, practice and assessment exercises (including automated testing), mini-lectures, and daily inspirations. This team has also launched a program to recruit and train undergraduate tutorial assistants to increase learning support. For more information on SPARC, contact Jeff Offutt at offutt@gmu.edu.The MaGE Peer Mentor program at Mount Holyoke College is addressing its increasing CS student enrollment by preparing undergraduate peer mentors to provide effective feedback on coding assignments and contribute to an inclusive learning environment. One of the major elements of these program is an online course that helps to recruit and train students to be undergraduate peer mentors. Mount Holyoke has made their entire online course curriculum for the peer mentor program available so that other institutions can incorporate all or part of it to assist with preparing their own student tutors. For more information on the MaGE curriculum, contact Heather Pon-Barry at ponbarry@mtholyoke.edu.MaGE Program Students and Faculty from Mount Holyoke CollegeAt University of California, Berkeley, the CS Capacity team is focused on providing access to increased and better tutoring. They’ve instituted a small-group tutoring program that includes weekend mastery learning sessions, increased office hours support, designated discussions section, project checkpoint deadlines, exam/homework/lab/discussion walkthrough videos, and a new office hours app that tracks student satisfaction with office hours. For more information on Berkeley’s interventions, contact Josh Hug at hug@cs.berkeley.edu.The CS Capacity team at Rutgers has been exploring the gender gap at multiple levels using a longitudinal study across four required CS classes (paper to be published in the proceedings of the SIGCSE 2017 Technical Symposium). They’re investigating several factors that may impact the re[...]



An updated YouTube-8M, a video understanding challenge, and a CVPR workshop. Oh my!

2017-02-15T13:00:21.093-08:00

Posted by Paul Natsev, Software EngineerLast September, we released the YouTube-8M dataset, which spans millions of videos labeled with thousands of classes, in order to spur innovation and advancement in large-scale video understanding. More recently, other teams at Google have released datasets such as Open Images and YouTube-BoundingBoxes that, along with YouTube-8M, can be used to accelerate image and video understanding. To further these goals, today we are releasing an update to the YouTube-8M dataset, and in collaboration with Google Cloud Machine Learning and kaggle.com, we are also organizing a video understanding competition and an affiliated CVPR’17 Workshop.An Updated YouTube-8MThe new and improved YouTube-8M includes cleaner and more verbose labels (twice as many labels per video, on average), a cleaned-up set of videos, and for the first time, the dataset includes pre-computed audio features, based on a state-of-the-art audio modeling architecture, in addition to the previously released visual features. The audio and visual features are synchronized in time, at 1-second temporal granularity, which makes YouTube-8M a large-scale multi-modal dataset, and opens up opportunities for exciting new research on joint audio-visual (temporal) modeling. Key statistics on the new version are illustrated below (more details here).A tree-map visualization of the updated YouTube-8M dataset, organized into 24 high-level verticals, including the top-200 most frequent entities, plus the top-5 entities for each vertical.Sample videos from the top-18 high-level verticals in the YouTube-8M dataset.The Google Cloud & YouTube-8M Video Understanding Challenge We are also excited to announce the Google Cloud & YouTube-8M Video Understanding Challenge, in partnership with Google Cloud and kaggle.com. The challenge invites participants to build audio-visual content classification models using YouTube-8M as training data, and to then label ~700K unseen test videos. It will be hosted as a Kaggle competition, sponsored by Google Cloud, and will feature a $100,000 prize pool for the top performers (details here). In order to enable wider participation in the competition, Google Cloud is also offering credits so participants can optionally do model training and exploration using Google Cloud Machine Learning. Open-source TensorFlow code, implementing a few baseline classification models for YouTube-8M, along with training and evaluation scripts, is available at Github. For details on getting started with local or cloud-based training, please see our README and the getting started guide on Kaggle. The CVPR 2017 Workshop on YouTube-8M Large-Scale Video Understanding We will announce the results of the challenge and host invited talks by distinguished researchers at the 1st YouTube-8M Workshop, to be held July 26, 2017, at the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) in Honolulu, Hawaii. The workshop will also feature presentations by top-performing challenge participants and a selected set of paper submissions. We invite researchers to submit papers describing novel research, experiments, or applications based on YouTube-8M dataset, including papers summarizing their participation in the above challenge.We designed this dataset with scale and diversity in mind, and hope lessons learned here will generalize to many video domains (YouTube-8M captures over 20 diverse video domains). We believe the challenge can also accelerate research by enabling researchers[...]



Announcing TensorFlow 1.0

2017-02-16T09:29:29.659-08:00

Posted by Amy McDonald Sandjideh, Technical Program Manager, TensorFlowIn just its first year, TensorFlow has helped researchers, engineers, artists, students, and many others make progress with everything from language translation to early detection of skin cancer and preventing blindness in diabetics. We’re excited to see people using TensorFlow in over 6000 open-source repositories online. Today, as part of the first annual TensorFlow Developer Summit, hosted in Mountain View and livestreamed around the world, we’re announcing TensorFlow 1.0:It’s faster: TensorFlow 1.0 is incredibly fast! XLA lays the groundwork for even more performance improvements in the future, and tensorflow.org now includes tips & tricks for tuning your models to achieve maximum speed. We’ll soon publish updated implementations of several popular models to show how to take full advantage of TensorFlow 1.0 - including a 7.3x speedup on 8 GPUs for Inception v3 and 58x speedup for distributed Inception v3 training on 64 GPUs!It’s more flexible: TensorFlow 1.0 introduces a high-level API for TensorFlow, with tf.layers, tf.metrics, and tf.losses modules. We’ve also announced the inclusion of a new tf.keras module that provides full compatibility with Keras, another popular high-level neural networks library.It’s more production-ready than ever: TensorFlow 1.0 promises Python API stability (details here), making it easier to pick up new features without worrying about breaking your existing code. Other highlights from TensorFlow 1.0:Python APIs have been changed to resemble NumPy more closely. For this and other backwards-incompatible changes made to support API stability going forward, please use our handy migration guide and conversion script.Experimental APIs for Java and GoHigher-level API modules tf.layers, tf.metrics, and tf.losses - brought over from tf.contrib.learn after incorporating skflow and TF SlimExperimental release of XLA, a domain-specific compiler for TensorFlow graphs, that targets CPUs and GPUs. XLA is rapidly evolving - expect to see more progress in upcoming releases.Introduction of the TensorFlow Debugger (tfdbg), a command-line interface and API for debugging live TensorFlow programs.New Android demos for object detection and localization, and camera-based image stylization.Installation improvements: Python 3 docker images have been added, and TensorFlow’s pip packages are now PyPI compliant. This means TensorFlow can now be installed with a simple invocation of pip install tensorflow.We’re thrilled to see the pace of development in the TensorFlow community around the world. To hear more about TensorFlow 1.0 and how it’s being used, you can watch the TensorFlow Developer Summit talks on YouTube, covering recent updates from higher-level APIs to TensorFlow on mobile to our new XLA compiler, as well as the exciting ways that TensorFlow is being used:Click here for a link to the livestream and video playlist (individual talks will be posted online later in the day).The TensorFlow ecosystem continues to grow with new techniques like Fold for dynamic batching and tools like the Embedding Projector along with updates to our existing tools like TensorFlow Serving. We’re incredibly grateful to the community of contributors, educators, and researchers who have made advances in deep learning available to everyone. We look forward to working with you on forums like GitHub issues, Stack Overflow, @TensorFlow, the discuss@tensorflow.org group and at [...]



On-Device Machine Intelligence

2017-02-09T11:00:06.281-08:00

Posted by Sujith Ravi, Staff Research Scientist, Google ResearchTo build the cutting-edge technologies that enable conversational understanding and image recognition, we often apply combinations of machine learning technologies such as deep neural networks and graph-based machine learning. However, the machine learning systems that power most of these applications run in the cloud and are computationally intensive and have significant memory requirements. What if you want machine intelligence to run on your personal phone or smartwatch, or on IoT devices, regardless of whether they are connected to the cloud?Yesterday, we announced the launch of Android Wear 2.0, along with brand new wearable devices, that will run Google's first entirely “on-device” ML technology for powering smart messaging. This on-device ML system, developed by the Expander research team, enables technologies like Smart Reply to be used for any application, including third-party messaging apps, without ever having to connect with the cloud…so now you can respond to incoming chat messages directly from your watch, with a tap.The research behind this began last year while our team was developing the machine learning systems that enable conversational understanding capability in Allo and Inbox. The Android Wear team reached out to us and was interested to know whether it would be possible to deploy this Smart Reply technology directly onto a smart device. Because of the limited computing power and memory on smart devices, we quickly realized that it was not possible to do so. Our product manager, Patrick McGregor, realized that this presented a unique challenge and an opportunity for the Expander team to return to the drawing board to design a completely new, lightweight, machine learning architecture — not only to enable Smart Reply on Android Wear, but also to power a wealth of other on-device mobile applications. Together with Tom Rudick, Nathan Beach, and other colleagues from the Android Wear team, we set out to build the new system.Learning with ProjectionsA simple strategy to build lightweight conversational models might be to create a small dictionary of common rules (input → reply mappings) on the device and use a naive look-up strategy at inference time. This can work for simple prediction tasks involving a small set of classes using a handful of features (such as binary sentiment classification from text, e.g. “I love this movie” conveys a positive sentiment whereas the sentence “The acting was horrible” is negative). But, it does not scale to complex natural language tasks involving rich vocabularies and the wide language variability observed in chat messages. On the other hand, machine learning models like recurrent neural networks (such as LSTMs), in conjunction with graph learning, have proven to be extremely powerful tools for complex sequence learning in natural language understanding tasks, including Smart Reply. However, compressing such rich models to fit in device memory and produce robust predictions at low computation cost (rapidly on-demand) is extremely challenging. Early experiments with restricting the model to predict only a small handful of replies or using other techniques like quantization or character-level models did not produce useful results.Instead, we built a different solution for the on-device ML system. We first use a fast, efficient mechanism to group similar incoming messages and project them to simila[...]



Announcing TensorFlow Fold: Deep Learning With Dynamic Computation Graphs

2017-02-10T09:54:48.252-08:00

Posted by Moshe Looks, Marcello Herreshoff and DeLesley Hutchins, Software EngineersIn much of machine learning, data used for training and inference undergoes a preprocessing step, where multiple inputs (such as images) are scaled to the same dimensions and stacked into batches. This lets high-performance deep learning libraries like TensorFlow run the same computation graph across all the inputs in the batch in parallel. Batching exploits the SIMD capabilities of modern GPUs and multi-core CPUs to speed up execution. However, there are many problem domains where the size and structure of the input data varies, such as parse trees in natural language understanding, abstract syntax trees in source code, DOM trees for web pages and more. In these cases, the different inputs have different computation graphs that don't naturally batch together, resulting in poor processor, memory, and cache utilization. Today we are releasing TensorFlow Fold to address these challenges. TensorFlow Fold makes it easy to implement deep-learning models that operate over data of varying size and structure. Furthermore, TensorFlow Fold brings the benefits of batching to such models, resulting in a speedup of more than 10x on CPU, and more than 100x on GPU, over alternative implementations. This is made possible by dynamic batching, introduced in our paper Deep Learning with Dynamic Computation Graphs.This animation shows a recursive neural network run with dynamic batching. Operations with the same color are batched together, which lets TensorFlow run them faster. The Embed operation converts words to vector representations. The fully connected (FC) operation combines word vectors to form vector representations of phrases. The output of the network is a vector representation of an entire sentence. Although only a single parse tree of a sentence is shown, the same network can run, and batch together operations, over multiple parse trees of arbitrary shapes and sizes.The TensorFlow Fold library will initially build a separate computation graph from each input.Because the individual inputs may have different sizes and structures, the computation graphs may as well. Dynamic batching then automatically combines these graphs to take advantage of opportunities for batching, both within and across inputs, and inserts additional instructions to move data between the batched operations (see our paper for technical details). To learn more, head over to our github site. We hope that TensorFlow Fold will be useful for researchers and practitioners implementing neural networks with dynamic computation graphs in TensorFlow. AcknowledgementsThis work was done under the supervision of Peter Norvig. [...]



Advancing Research on Video Understanding with the YouTube-BoundingBoxes Dataset

2017-02-07T12:33:21.053-08:00

Posted by Esteban Real, Vincent Vanhoucke, Jonathon Shlens, Google Brain team andStefano Mazzocchi, Google ResearchOne of the most challenging research areas in machine learning today is enabling computers to understand what a scene is about. For example, while humans know that a ball that disappears behind a wall only to reappear a moment later is very likely the same object, this is not at all obvious to an algorithm. Understanding this requires not only a global picture of what objects are contained in each frame of a video, but also where those objects are located within the frame and their locations over time. Just last year we published YouTube-8M, a dataset consisting of automatically labelled YouTube videos. And while this helps further progress in the field, it is only one piece to the puzzle. Today, in order to facilitate progress in video understanding research, we are introducing YouTube-BoundingBoxes, a dataset consisting of 5 million bounding boxes spanning 23 object categories, densely labeling segments from 210,000 YouTube videos. To date, this is the largest manually annotated video dataset containing bounding boxes, which track objects in temporally contiguous frames. The dataset is designed to be large enough to train large-scale models, and be representative of videos captured in natural settings. Importantly, the human-labelled annotations contain objects as they appear in the real world with partial occlusions, motion blur and natural lighting.Summary of dataset statistics. Bar Chart: Relative number of detections in existing image (red) and video (blue) data sets. The YouTube BoundingBoxes dataset (YT-BB) is at the bottom, is at the bottom. Table: The three columns are counts for: classification annotations, bounding boxes, and unique videos with bounding boxes. Full details on the dataset can be found in the preprint.A key feature of this dataset is that bounding box annotations are provided for entire video segments. These bounding box annotations may be used to train models that explicitly leverage this temporal information to identify, localize and track objects over time. In a video, individual annotated objects might become entirely occluded and later return in subsequent frames. These annotations of individual objects are sometimes not recognizable from individual frames, but can be understood and recognized in the context of the video if the objects are localized and tracked accurately. Three video segments, sampled at 1 frame per second. The final frame of each example shows how it is visually challenging to recognize the bounded object, due to blur or occlusion (train example, blue arrow). However, temporally-related frames, where the object has been more clearly identified, can allow object classes to be inferred. Note how only visible parts are included in the box: the orange arrow in the bear example (middle row) points to the hidden head. The dog example illustrates tight bounding boxes that track the tail (orange arrows) and foot (blue arrows). The airplane example illustrates how partial objects are annotated (first frame) tracked across changes in perspective, occlusions and camera cuts.We hope that this dataset might ultimately aid the computer vision and machine learning community and lead to new methods for analyzing and understanding real world vision problems. You can learn more about the dataset in this associated preprint.Ac[...]