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The latest from Google Research
Top Shot on Pixel 3
Thursday, December 20, 2018
Posted by Li Zhang and Wei (Alex) Hong, Software Engineers
Life is full of meaningful moments — from a child’s first step to an impromptu jump for joy — that one wishes could be preserved with a picture. However, because these moments are often unpredictable, missing that perfect shot is a frustrating problem that smartphone camera users face daily. Using our experience from developing
Google Clips
, we wondered if we could develop new techniques for the Pixel 3 camera that would allow everyone to capture the perfect shot every time.
Top Shot
is a new feature recently launched with
Pixel 3
that helps you to capture precious moments precisely and automatically at the press of the shutter button. Top Shot saves and analyzes the image frames before and after the shutter press on the device in real-time using computer vision techniques, and recommends several alternative high-quality
HDR+
photos.
Examples of Top Shot on Pixel 3. On the left, a better smiling shot is recommended. On the right, a better jump shot is recommended. The recommended images are high-quality HDR+ shots.
Capturing Multiple Moments
When a user opens the Pixel 3 Camera app, Top Shot is enabled by default, helping to capture the perfect moment by analyzing images taken both before and after the shutter press. Each image is analyzed for some qualitative features (e.g., whether the subject is smiling or not) in real-time and entirely on-device to preserve privacy and minimize latency. Each image is also associated with additional signals, such as
optical flow
of the image, exposure time, and gyro sensor data to form the input features used to score the frame quality.
When you press the shutter button, Top Shot captures up to 90 images from 1.5 seconds before and after the shutter press, selecting up to two alternative shots to save in high resolution — the original shutter frame and high-res alternatives for you to review (other lower-res frames can also be reviewed as desired). The shutter frame is processed and saved first. The best alternative shots are saved afterwards.
Google’s Visual Core
on Pixel 3 is used to process these top alternative shots as HDR+ images with a very small amount of extra latency, and are embedded into the file of the
Motion Photo
.
Top-level diagram of Top Shot capture.
Given Top Shot runs in the camera as a background process, it must have very low power consumption. As such, Top Shot uses a hardware-accelerated
MobileNet
-based
single shot detector
(SSD). The execution of such optimized models is also throttled by power and thermal limits.
Recognizing Top Moments
When we set out to understand how to enable people to capture the best moments with their camera, we focused on three key attributes: 1) functional qualities like lighting, 2) objective attributes (are the subject's eyes open? Are they smiling?), and 3) subjective qualities like emotional expressions. We designed a computer vision model to recognize these attributes while operating in a low-latency, on-device mode.
During our development process, we started with a vanilla MobileNet model and set out to optimize for Top Shot, arriving at a customized architecture that operated within our accuracy, latency and power tradeoff constraints. Our neural network design detects low-level visual attributes in early layers, like whether the subject is blurry, and then dedicates additional compute and parameters toward more complex objective attributes like whether the subject's eyes are open, and subjective attributes like whether there is an emotional expression of amusement or surprise. We trained our model using
knowledge distillation
over a large number of diverse face images using
quantization
during both training and inference.
We then adopted a layered
Generalized Additive Model
(GAM) to provide quality scores for faces and combine them into a weighted-average “frame faces” score. This model made it easy for us to interpret and identify the exact causes of success or failure, enabling rapid iteration to improve the quality and performance of our attributes model. The number of free parameters was on the order of dozens, so we could optimize these using Google's black box optimizer,
Vizier
, in tandem with any other parameters that affected selection quality.
Frame Scoring Model
While Top Shot prioritizes for face analysis, there are good moments in which faces are not the primary subject. To handle those use cases, we include the following additional scores in the overall frame quality score:
Subject motion saliency score
— the low-resolution optical flow between the current frame and the previous frame is estimated in ISP to determine if there is salient object motion in the scene.
Global motion blur score
— estimated from the camera motion and the exposure time. The camera motion is calculated from sensor data from the gyroscope and OIS (optical image stabilization).
“3A” scores
— the status of auto exposure, auto focus, and auto white balance, are also considered.
All the individual scores are used to train a model predicting an overall quality score, which matches the frame preference of human raters, to maximize end-to-end product quality.
End-to-End Quality and Fairness
Most of the above components are each evaluated for accuracy independently However, Top Shot presents requirements that are uniquely challenging since it’s running real-time in the Pixel Camera. Additionally, we needed to ensure that all these signals are combined in a system with favorable results. That means we need to gauge our predictions against what our users perceive as the “top shot.”
To test this, we collected data from hundreds of volunteers, along with their opinions of which frames (out of up to 90!) looked best. This donated dataset covers many typical use cases, e.g. portraits, selfies, actions, landscapes, etc.
Many of the 3-second clips provided by Top Shot had more than one good shot, so it was important for us to engineer our quality metrics to handle this. We used some modified versions of traditional
Precision and Recall
, some classic ranking metrics (such as
Mean Reciprocal Rank
), and a few others that were designed specifically for the Top Shot task as our objective. In addition to these metrics, we additionally investigated causes of image quality issues we saw during development, leading to improvements in avoiding blur, handling multiple faces better, and more. In doing so, we were able to steer the model towards a set of selections people were likely to rate highly.
Importantly, we tested the Top Shot system for fairness to make sure that our product can offer a consistent experience to a very wide range of users. We evaluated the accuracy of each signal used in Top Shot on several different subgroups of people (based on gender, age, ethnicity, etc), testing for accuracy of each signal across those subgroups.
Conclusion
Top Shot is just one example of how Google leverages optimized hardware and cutting-edge machine learning to provide useful tools and services. We hope you’ll find this feature useful, and we’re committed to further improving the capabilities of mobile phone photography!
Acknowledgements
This post reflects the work of a large group of Google engineers, research scientists, and others including: Ari Gilder, Aseem Agarwala, Brendan Jou, Chris Breithaupt, David Karam, Eric Penner, Farooq Ahmad, Henri Astre, Hillary Strickland, John Zhang, Marius Renn, Matt Bridges, Maxwell Collins, Navid Shiee, Ryan Gordon, Sarah Clinckemaillie, Shu Zhang, Vivek Kesarwani, Xuhui Jia, Yukun Zhu and Yuzo Watanabe.
Google AI Princeton: Current and Future Research
Tuesday, December 18, 2018
Posted by Elad Hazan and Yoram Singer, Research Scientists, Google AI and Princeton University
Google has long
partnered with academia
to advance research, collaborating with universities all over the world on joint research projects which result in novel developments in Computer Science, Engineering, and related fields. Today we announce the latest of these academic partnerships in the form of a new lab, across the street from Princeton University’s historic Nassau Hall, opening early next year. By fostering closer collaborations with faculty and students at Princeton, the lab aims to broaden research in multiple facets of machine learning, focusing its initial research efforts on optimization methods for
large-scale machine learning
,
control theory
and
reinforcement learning
. Below we give a brief overview of the research progress thus far.
Large-Scale Optimization
Imagine you have gone for a mountain hike and have run out of water. You need to get to a lake. How can you do so most efficiently? This is a matter of optimizing your route, and the mathematical analogue of this is the
gradient descent method
. You therefore move in the direction of steepest descent until you find the nearest lake at the bottom of your path. In the language of optimization, the location of the lake is referred to as a (local) minimum. The trajectory of gradient descent resembles the path, shown below, a thirsty yet avid hiker would take in order to get down to a lake as fast as she can.
Gradient descent (GD), and its randomized version,
stochastic gradient descent
(SGD), are the methods of choice for optimizing the weights of neural networks. Stacking all of the parameters together, we form a set of cells organized into vectors Let us take a simplistic view and assume that our neural net merely has 5 different parameters. Taking a gradient descent step amounts to subtracting the gradient vector (red) from the current set of parameters (blue) and putting the result back into the parameter vector.
Going back to our avid hiker, suppose she finds an unmarked path that is long and narrow, with limited visibility as she gazes down. If she follows the descent method her path would zig-zag down the hill, as shown in the illustration below on the left. However, she can now make faster progress by exploiting the skewed geometry of the terrain. That is, she can make a bigger leap forward than to the sides. In the context of gradient descent, pacing up is called acceleration. A popular class of acceleration methods is named adaptive regularization, or adaptive preconditioning, first introduced by the
AdaGrad
algorithm devised in collaboration with Prof. John Duchi from Stanford while he was at Google.
The idea is to change the geometry of the landscape of the optimization objective to make it easier for gradient descent to work. In order to do so, preconditioning methods stretch and rotate the space. The terrain after preconditioning looks like the serene, perfectly spherical lake above on the right, and the descent trajectory is a straight line! Procedurally, instead of subtracting the gradient vectors from the parameters vector per-se, adaptive preconditioning first multiplies the gradient by a 5×5 multicell structure, called a matrix preconditioner, as shown below.
This preconditioning operation yields a stretched and rotated gradient which is then subtracted as before, allowing much faster progress toward a basin. However, there is a downside to preconditioning, namely, its computational cost. Instead of subtracting a 5-dimensional gradient vector from a 5-dimensional parameter vector, the preconditioning transformation itself requires 5×5=25 operations. Suppose we would like to precondition gradients in order to learn a deep network with 10 million parameters. A single preconditioning step would require 100 trillion operations. In order to save computation, a diagonal version in which preconditioning amounts to stretching sans rotation was also introduced in the original
AdaGrad
paper. The diagonal version was later adopted and modified, yielding another very successful algorithm called
Adam
.
This simplified diagonal preconditioning imposes only a marginal additional cost to gradient descent. However, oversimplification has its own downside: we are no longer able to rotate our space. Going back to our hiker, if the deep-and-narrow canyon runs from southeast to northwest, she can no longer take large westward leaps. Had we provided her with a “rigged” compass in which the north pole is in the northwest, she could have followed her descent procedure as before. In high dimensions, the analog of compass rigging is full-matrix preconditioning. We thus asked ourselves whether we could devise a preconditioning method that is computationally efficient while allowing for the equivalent of coordinate rotations.
At Google AI Princeton, we
developed a new method
for full-matrix adaptive preconditioning at roughly the same computational cost as the commonly-used diagonal restriction. Details can be found in the paper, but the key idea behind the method is depicted below. Instead of using a full matrix, we replace the preconditioning matrix by a product of three matrices: a tall & thin matrix, a (small) square matrix, and a short & fat matrix. The vast amount of computation is performed using the smaller matrix. If we have
d
parameters, instead of a single large
d
×
d
matrix, the matrices maintained by GGT (shorthand for the operation
Gradient Gradient
T
), the proposed method, are of sizes
d
×
k
,
k
×
k
,
k
×
d
respectively.
For reasonable choices of
k
, which can be thought of as the “window size” of the algorithm, the computational bottleneck has been mitigated from a single large matrix, to that of a much smaller
kk
matrix. In our implementation we typically choose
k
to be, say, 50, and maintaining the smaller square matrix is significantly less expensive while yielding good empirical performance. When compared to other adaptive methods on standard deep learning tasks, GGT is competitive with AdaGrad and Adam.
Spectral Filtering for Control and Reinforcement Learning
Another broad mission of Google’s research group in Princeton is to develop principled building blocks for decision-making systems. In particular, the group strives to leverage provable guarantees from the field of online learning, which studies the robust (worst-case) guarantees of decision-making algorithms under uncertainty. An online algorithm is said to attain a no-regret guarantee if it learns to make decisions as well as the best "offline" decision in hindsight. Ideas from this field have already enabled many innovations within
theoretical computer science
, and provide a mathematically elegant framework to study a widely-used technique called boosting. We envision using ideas from online learning to broaden the toolkit of modern reinforcement learning.
With that goal in mind, and in collaboration with researchers and students at Princeton, we developed the algorithmic technique of
spectral filtering
for estimation and control of linear dynamical systems (see
several
recent
publications
). In this setting, noisy observations (e.g., location sensor measurements) are being streamed from an unknown source. The source of the signal is a system whose state evolves over time following a set of linear equations (e.g. Newton's laws). To forecast future signals (prediction), or to perform actions which bring the system to a desired state (control), the usual approach starts with learning the model explicitly (a task termed
system identification
), which is often slow and inaccurate.
Spectral filtering circumvents the need to model the dynamics explicitly, by reformulating prediction and control as convex programs, enabling provable no-regret guarantees. A major component of the technique is that of a new signal processing transformation. The idea is to summarize the long history of past input signals through convolution with a tailored bank of filters, and then use this representation to predict the dynamical system’s future outputs. Each filter compresses the input signal into a single real number, by taking a weighted combination of the previous inputs.
A set of filters depicted in a plot of filter amplitude versus time. With our technique of spectral filtering, multiple filters are used to predict the state of a linear dynamical system at any given time. Each filter is a set of weights used to summarize past observations, such that combining them in a weighted fashion, over time allows us to accurately predict the system.
The mathematical derivation of these weights (filters) has an interesting connection to the spectral theory of
Hankel matrices
.
Looking Forward
We are excited about the progress we have made thus far in partnership with Princeton’s faculty and students, and we look forward to the official opening of the lab in the coming weeks. It has long been Google’s view that both industry and academia benefit significantly from an open research culture, and we look forward to our continued close collaboration.
Acknowledgments
The research and results discussed in this post would not have been possible without contributions from the following researchers: Naman Agarwal, Brian Bullins, Xinyi Chen, Udaya Ghai, Tomer Koren, Karan Singh, Cyril Zhang, Yi Zhang, and visiting professor Sham Kakade. Since joining Google earlier this year, the research team has been working remotely from both the Google NYC office as well as the Princeton University campus, and they look forward to moving into the new Google space across from the Princeton campus in the weeks to come.
Exploring Quantum Neural Networks
Monday, December 17, 2018
Posted by Jarrod McClean, Senior Research Scientist and Hartmut Neven, Director of Engineering, Google AI Quantum Team
Since its inception, the
Google AI Quantum team
has pushed to understand the role of quantum computing in
machine learning
. The existence of algorithms with provable advantages for
global optimization
suggest that quantum computers may be useful for training existing models within machine learning more quickly, and we are building
experimental quantum computers
to investigate how intricate quantum systems can carry out these computations. While this may prove invaluable, it does not yet touch on the tantalizing idea that quantum computers might be able to provide a way to learn more about
complex patterns in physical systems
that conventional computers cannot in any reasonable amount of time.
Today we talk about two recent papers from the Google AI Quantum team that make progress towards understanding the power of quantum computers for learning tasks. The first constructs a quantum model of neural networks to investigate how a popular classification task might be carried out on quantum processors. In the second paper, we show how peculiar features of quantum geometry change the strategies for training these networks in comparison to their classical counterparts, and offer guidance towards more robust training of these networks.
In “
Classification with Quantum Neural Networks on Near Term Processors
”, we construct a model of quantum neural networks (QNNs) that is specifically designed to work on quantum processors that are expected to be available in the near term. While the current work is primarily theoretical, their structure facilitates implementation and testing on quantum computers in the immediate future. These QNNs can be adapted through supervised learning of labeled data, and we show that it is possible to train a QNN to classify images in the famous MNIST dataset. Follow up work in this area with larger quantum devices may pit the ability of quantum networks to learn patterns against popular classical networks.
Quantum Neural Network for classification. Here we depict a sample quantum neural network, where in contrast to hidden layers in classical deep neural networks, the boxes represent entangling actions, or “quantum gates”, on qubits. In a superconducting qubit setup this could be enacted through a microwave control pulse corresponding to each box.
In “
Barren Plateaus in Quantum Neural Network Training Landscapes
”, we focus on the training of quantum neural networks, and probe questions related to a key difficulty in classical neural networks, which is the problem of vanishing or exploding gradients. In conventional neural networks, a good unbiased initial guess for the neuron weights
often involves randomization
, although there can be some
difficulties
as well. Our paper shows that peculiar features of quantum geometry unequivocally prevent this from being a good strategy in the quantum case, instead taking you to barren plateaus. The implications of this work may guide future strategies for initializing and training quantum neural networks.
QNN vanishing gradient: concentration of measure in high dimensional spaces. In very high dimensional spaces, such as those explored by quantum computers, the vast majority of states counterintuitively sit near the equator of the hypersphere (left). This means that any smooth function on this space will tend to take a value very close to its mean with overwhelming probability when selected at random (right).
This research sets the stage for improvements in both the construction and training of quantum neural networks. In particular, experimental realizations of quantum neural networks using hardware at Google will enable rapid exploration of quantum neural networks in the near term. We hope that the insights from the geometry of these states will lead to new algorithms to train these networks that will be essential to unlocking their full potential.
Improving the Effectiveness of Diabetic Retinopathy Models
Thursday, December 13, 2018
Posted by Rory Sayres PhD and Jonathan Krause PhD, Google AI, Healthcare
Two years ago, we announced
our inaugural work in training deep learning models
for diabetic retinopathy (DR), a complication of diabetes that is one of the fastest growing causes of vision loss. Based on this research, we set out to
apply our technology to improve health outcomes in the world
. At the same time, we’ve continued our efforts to improve the model’s performance, explainability, and applicability in clinical settings. Today, we are sharing our research progress toward these goals, as well as
announcing a new partner
in Thailand.
Improving Model Performance with High-quality Labels
The performance of DR deep learning models is critically important, especially when subtle errors have the potential to generate a misdiagnosis. Earlier this year we published
a paper
in the journal
Ophthalmology
that looked at how we could improve our model by 1) moving toward a more granular 5-point grading scale (versus the previous 2-class system) and 2) incorporating adjudication by a panel of retinal specialists. During the adjudication process, a group of retinal specialists debated any case with disagreement until everyone agreed on the final grade. Compared to simply taking a majority vote, this method of resolving disagreements was more accurate and allowed for the identification of subtle findings, such as
microaneurysms
.
To increase the efficiency of the adjudication process, we carefully selected a small subset (0.22%) of images to use as a tuning set, substantially improving model performance by optimizing model hyperparameters on this more accurate reference standard. When we subsequently measured the rate of agreement against a test set of images with an adjudicated reference standard, the kappa scores (a measurement of agreement that ranges from 0 [random] to 1 [perfect agreement]) for individual retinal specialists, ophthalmologists, and the algorithm ranged from 0.82-0.91, 0.80-0.84, and 0.84, respectively.
Making our Models More Transparent
As we deploy this technology, it is important that we take the proper steps to ensure that it is transparent and trusted. To that end, we have been exploring ways to explain how the model is making its predictions, with the goal of making the DR model a better diagnostic tool and aid for doctors.
In
our latest study
, to be published today in
Ophthalmology
, we demonstrate methods by which explanations of deep learning algorithms can be shown to ophthalmologists to increase both the accuracy and confidence of their grading for diabetic eye disease. Using the results of the model trained and validated on high quality labels from our earlier study, we generated different forms of potential assistance for general ophthalmologists. We presented to the physicians the algorithm’s predicted scores for different DR severity levels as well as heatmaps highlighting image regions that most strongly drove its predictions. Using this assistance, we saw a significant increase in physicians’ diagnostic accuracy, as well as improved confidence in their diagnosis.
We saw clear evidence that showing model predictions could help physicians catch pathology they otherwise might have missed. In the retinal image below, our adjudication panel found signs of vision-threatening DR. This was missed by 2 of 3 doctors who graded it without assistance; but caught by all 3 doctors who graded it when they saw the model predictions (which accurately detected the pathology).
On the left is a fundus image graded as having proliferative (vision-threatening) DR by an adjudication panel of ophthalmologists (ground truth). On the top right is an illustration of our deep learning model’s predicted scores (“P” = proliferative, the most severe form of DR). On the bottom right is the set of grades given by physicians without assistance (“Unassisted”) and those who saw the model’s predictions (“Grades Only”).
We also saw evidence that physicians and the model can work together in a way that provides more accuracy than either individually. In the retinal image below, our adjudication panel of retina specialists considered it to have moderate DR. Without assistance, two out of three ophthalmologists grading the image marked it as no DR. In real-world settings, this situation could result in a patient missing a needed referral to a specialist.
On the left is a retinal fundus image graded as having moderate DR (“Mo”) by an adjudication panel of ophthalmologists (ground truth). On the top right is an illustration of the predicted scores (“N” = no DR, “Mi” = Mild DR, “Mo” = Moderate DR) from the model. On the bottom right is the set of scores given by physicians without assistance (“Unassisted”) and those who saw the model’s predictions (“Grades Only”).
In this particular case, our model also indicated evidence for no DR. However, when ophthalmologists saw the model’s predictions, all three gave the correct answer. Seeing that the model saw some evidence for Moderate -- even if it wasn’t the highest score -- may prompt doctors to examine particular cases more carefully for pathology they may otherwise miss. We are excited to develop assistance that works like this, where human and machine learning abilities complement each other.
A New Partner in our Global Efforts
With the help of screening programs and in collaboration with
Verily
, we have laid a robust foundation for the implementation of these highly accurate systems in real world clinical settings. Working with doctors at Aravind Eye Hospitals and Sankara Nethralaya in India, and now, through our new partnership with the
Rajavithi Hospital
, affiliated with the Department of Medical Services, Ministry of Public Health in Thailand, we are validating the model performance with patients from broad screening programs. Given the positive results of our model on their real patient population, we are now beginning to pilot the model in their screening programs. We’re looking forward to a very busy 2019!
Grasp2Vec: Learning Object Representations from Self-Supervised Grasping
Tuesday, December 11, 2018
Posted by Eric Jang, Software Engineer, Robotics at Google and Coline Devin, Berkeley PhD Student and former Research Intern
From a remarkably young age, people are capable of recognizing their favorite objects and picking them up, despite never being explicitly taught how to do so. According to
cognitive developmental research
, the ability to interact with objects in the world plays a crucial role in the emergence of object perception and manipulation capabilities, such as targeted grasping. By interacting with the world around them, people are able to learn with
self-supervision:
we know what actions we took, and we learn from the outcome. In robotics, this type of self-supervised learning is actively researched because it enables robotic systems to learn without the need for large amounts of training data or manual supervision.
Inspired by the concept of
object permanence
, we propose
Grasp2Vec
, a simple yet highly effective algorithm for acquiring object representations. Grasp2Vec is based on the intuition that an attempt to pick up anything provides several pieces of information — if a robot grasps an object and holds it up, the object had to be in the scene before the grasp. Furthermore, the robot knows that the object it grasped is currently in its gripper, and therefore has been removed from the scene. By using this form of self supervision, the robot can learn to recognize the object by the visual change in the scene after the grasp.
Building on
our prior collaboration with X Robotics
, where a series of robots learn in parallel to grasp household objects using only monocular camera inputs, we use a robotic arm to grasp objects “unintentionally”, and that experience enables the learning of a rich representation of objects. These representations can then be used to acquire “intentional grasping” capabilities, where the robot arm can then pick up user-commanded objects.
Constructing a Perceptual Reward Function
In the framework of
reinforcement learning
(RL), task success is measured via a “reward function”. By maximizing that reward, robots can teach themselves
diverse grasping skills from scratch
. Engineering a reward function is easy when success can be measured by simple sensor measurements. A simple example of this is a
button that supplies rewards directly to a robot when it is pushed
.
However, engineering a reward function is much more difficult when our success criteria depends on
perceptual understanding
of the task at hand. Consider the task of instance grasping, where a robot is presented a picture of a desired object being held in the gripper. After the robot attempts to grasp that object, it inspects the contents of the gripper. The reward function for this task comes down to answering the question of object recognition:
Do these objects match?
On the left, the gripper is holding the brush and there are some objects (yellow cup, blue plastic block) in the background. On the right, the gripper is holding the yellow cup and the brush is in the background. If the left image was the desired outcome, a good reward function should “understand” that the two images above correspond to different objects.
In order to solve this recognition problem, we need a perception system that extracts meaningful object concepts from unstructured image data (without any human annotations), learning the visual perception of objects in an unsupervised fashion. At their core, unsupervised learning algorithms work because they make structural assumptions about data. It is common to assume that images
can be compressed into a low-dimensional space
, and that
frames in a video can be predicted from previous frames
. However, without further assumptions on the content of the data, these are usually insufficient for learning disentangled object representations.
What if we used a robot to
physically
disentangle objects from each other during data collection? The field of robotics presents an exciting opportunity for representation learning because robots can manipulate objects, thus providing the factors of variation needed in data. Our method relies on the insight that grasping an object removes it from the scene. This yields 1) an image of the scene before grasping, 2) an image of the scene after grasping and 3) an isolated view of the grasped object itself.
Left: Objects before the grasp. Center: Objects after the grasp. Right: The Grasped object.
If we then consider an embedding function that extracts “the set of objects” from images, it should preserve the following subtractive relation:
objects_before_grasp - objects_after_grasp = grasped_object
We implement this equality relation using a fully convolutional architecture and a simple metric learning algorithm. At training time, the architecture shown below embeds the pre-grasp images and post-grasp images into a dense
spatial feature map
. The maps are mean-pooled into vectors and the difference between the “before grasp” and “after grasp” vectors represents a set of objects. This vector and the corresponding vector representation of the grasped object are pushed to equivalence via the
N-Pairs objective
.
Add caption
Once trained, two useful properties emerge naturally from our model.
1. Object Similarity
The first property is that a
cosine distance
between vector embeddings allows us to compare objects and determine whether they are identical. This can be used to implement reward functions for reinforcement learning, and allow robots to learn instance grasping without human-provided labels.
2. Localizing Target Objects
The second property is that we can combine scene spatial maps and object embeddings to localize a “query object” in image space. By taking the element-wise product of spatial feature maps and the vector corresponding to the query object, we can find all the pixels in the spatial map that “match” the query object.
Using Grasp2Vec embeddings to localize objects in a scene. The image on the top left shows the objects in the bin. On the bottom left is the query object we wish to grasp. By taking the dot product of the query object vector with the spatial features of the scene image, we get a per-pixel “activation map” (top right image) of how similar that region of the image is to the query. This response map can be used to approach the object for grasping.
Our method also works when there are multiple objects that match the query object, or even if the query consists of multiple objects (the average of two vectors). For example, here is a scenario where it detects multiple orange blocks in a scene.
The resulting “heatmap” can be used to plan the robot approach to the target object(s). We combine Grasp2Vec’s localization and instance recognition capabilities with our “grasp anything” policies to obtain a success rate of 80% on objects seen during data collection and 59% on novel objects the robot hasn’t encountered before.
Conclusion
In
our paper
, we show how robotic grasping skills can generate the data used for learning object-centric representations. We then can use representation learning to “bootstrap” more complex skills like instance grasping, all while retaining the self-supervised learning properties of our autonomous grasping system.
Besides our own work, a number of recent papers have also studied how self-supervised interaction can be used to acquire representations, by
grasping
,
pushing
, and otherwise
manipulating
objects in the environment. Going forward, we are excited not only for what machine learning can bring to robotics by way of better perception and control, but also what robotics can bring to machine learning in new paradigms of self-supervision.
Acknowledgements
This research was conducted by Eric Jang, Coline Devin, Vincent Vanhoucke, and Sergey Levine. We’d like to thank Adrian Li, Alex Irpan, Anthony Brohan, Chelsea Finn, Christian Howard, Corey Lynch, Dmitry Kalashnikov, Ian Wilkes, Ivonne Fajardo, Julian Ibarz, Ming Zhao, Peter Pastor, Pierre Sermanet, Stephen James, Tsung-Yi Lin, Yunfei Bai, and many others at Google, X, and the broader robotics community who contributed to improving this work.
Providing Gender-Specific Translations in Google Translate
Monday, December 10, 2018
Posted by Melvin Johnson, Senior Software Engineer, Google Translate
Over the past few years,
Google Translate
has made significant improvements to translation quality by switching to an
end-to-end neural network-based system
. At the same time, we realized that translations from our models can reflect societal biases, such as gender bias. Specifically, languages differ a lot in how they represent gender, and when there are ambiguities during translation, the systems tend to pick gender choices that reflect societal asymmetries, resulting in biased translations. For instance, Google Translate historically translated the Turkish equivalent of “
He/she is a doctor
” into the masculine form, and the Turkish equivalent of “
He/she is a nurse
” into the feminine form.
Recently,
we announced
that we’re taking the first step at reducing gender bias in our translations. We now provide both feminine and masculine translations when translating single-word queries from English to four different languages (French, Italian, Portuguese, and Spanish), and when translating phrases and sentences from Turkish to English.
Gender-specific translations on the Google Translate website.
Supporting gender-specific translations for single-word queries involved enriching our underlying dictionary with gender attributes. Supporting gender-specific translations for longer queries (phrases and sentences) was particularly challenging and involved making significant changes to our translation framework. For these longer queries, we focused initially on Turkish-to-English translation. We developed a three-step approach to solve the problem of providing a masculine and feminine translation in English for a gender-neutral query in Turkish.
Detecting Gender-Neutral Queries
Many Turkish sentences that refer to people are gender-neutral, but not all are. Detecting which queries are eligible for gender-specific translations is a hard problem because Turkish is
morphologically complex
, meaning that reference to a person can either be explicit with a gender-neutral pronoun (e.g.
O, Ona
) or implicitly encoded. For example, the sentence “
Biliyor mu?
” has no explicit gender-neutral pronoun but can be translated as either “
Does she know?
” or “
Does he know?
”. This complexity means that we cannot use a simple list of gender-neutral pronouns to detect gender-neutral Turkish queries and need a machine-learned system. We estimate that approximately 10% of Turkish Translate queries are ambiguous, and eligible for both feminine and masculine translations.
To detect these queries, we use state-of-the-art text classification algorithms (same as those used in our
Cloud Natural Language API
) to build a system that is able to detect when a given Turkish query is gender-neutral. Since this introduces an additional step before obtaining the translations, we had to carefully balance model complexity with latency. We trained our system on thousands of human-rated Turkish examples, where raters were asked to judge whether a given example is gender-neutral or not. Our final classification system is a convolutional neural network that can accurately detect queries which require gender-specific translations.
Generating Gender-Specific Translations
Next, we enhanced our underlying
Neural Machine Translation
(NMT) system to produce feminine and masculine translations when requested. When no gender is requested, we trained the model to produce the default translation. This involved:
Identifying and dividing our parallel training data into those with feminine words, those with masculine and those with ungendered words.
Adding an additional input token to the beginning of the sentence to specify the required gender to translate to, similar to how we build
multilingual NMT systems
:
<2MALE> O bir doktor → He is a doctor
<2FEMALE> O bir doktor → She is a doctor
Training our enhanced NMT model on the feminine, masculine and ungendered data sources. We experimented with various mixing ratios for these sources to enable the model to perform equally well on the three tasks.
If a user's query is determined to be gender-neutral, we add a gender prefix to the translation request. For these requests, our final NMT model can reliably produce feminine and masculine translations 99% of the time. Additionally, the system maintains translation quality on queries without the gender prefix.
Checking for Accuracy
Finally, we have a step that decides whether to display the gender-specific translations. Since the training data that produces the masculine translation is different from the training data that produces the feminine translation, there may be differences between the two translations unrelated to gender. If the gender-specific translations are determined to be low quality, we show only the single default translation. To determine the quality of the gender-specific translations, we verify:
If the requested feminine translation is feminine.
If the requested masculine translation is masculine.
If the feminine and masculine translations are exactly equivalent with the exception of gender-related changes. Even minor changes in the wording between the translations will result in being filtered.
Top:
The masculine and feminine translations differ only with respect to gender i.e. “he” and “his” vs “she” and “her”. Hence, we will show gender-specific translations.
Bottom:
The masculine and feminine translations differ correctly with respect to gender i.e. “he” vs “she”. However, the change from “really” to “actually” is not related to gender. Hence, we will filter gender-specific translations and display the default translation.
Putting it all together, input sentences first go through the classifier, which detects whether they’re eligible for gender-specific translations. If the classifier says “yes”, we send three requests to our enhanced NMT model—a feminine request, a masculine request and an ungendered request. Our final step takes into account all three responses and decides whether to display gender-specific translations or a single default translation. This step is still quite conservative in order to maximize the quality of gender-specific translations shown; hence our overall recall is only around 60%. We plan to increase our coverage and add support for more complex sentences in future iterations.
This is just the first step toward addressing gender bias in machine-translation systems and reiterates Google’s commitment to
fairness in machine learning
. In the future, we plan to extend gender-specific translations to more languages and to address non-binary gender in translations.
Acknowledgements:
This effort has been successful thanks to the hard work of a lot of people including, but not limited to, the following (in alphabetical order of last name): Lindsey Boran, HyunJeong Choe, Héctor Fernández Alcalde, Orhan Firat, Qin Gao, Rick Genter, Macduff Hughes, Tolga Kayadelen, James Kuczmarski, Tatiana Lando, Liu Liu, Michael Mandl, Nihal Meriç Atilla, Mengmeng Niu, Adnan Ozturel, Emily Pitler, Kathy Ray, John Richardson, Larissa Rinaldi, Alex Rudnick, Apu Shah, Jason Smith, Antonio Stella, Romina Stella, Jana Strnadova, Katrin Tomanek, Barak Turovsky, Dan Schwarz, Shilp Vaishnav, Clayton Watts, Kellie Webster, Colin Young, Pendar Yousefi, Candice Zhang and Min Zhao.
Adding Diversity to Images with Open Images Extended
Friday, December 7, 2018
Posted by Anurag Batra and Parker Barnes, Product Managers, Google AI
Recently,
we introduced
the
Inclusive Images Kaggle competition
, part of the
NeurIPS 2018 Competition Track
, with the goal of stimulating research into the effect of geographic skews in training datasets on ML model performance, and to spur innovation in developing more inclusive models. While the competition has concluded, the broader movement to build more diverse datasets is just beginning.
Today, we’re announcing
Open Images Extended
, a new branch of Google’s Open Images dataset, which is intended to be a collection of complementary datasets with additional images and/or annotations that better represent global diversity. The first set we are adding is the Crowdsourced extension which is seeded with 478K+ images donated by
Crowdsource app
users from all around the world.
About the Crowdsourced Extension of Open Images Extended
To bring greater geographic diversity to Open Images, we enabled the global community of Crowdsource app users to photograph the world around them and make their photos available to researchers and developers as part of the Open Images Extended dataset. A large majority of these images are from India, with some representation from the Middle East, Africa and Latin America.
The images, focus on some key categories like household objects, plants & animals, food, and people in various professions (all faces are blurred to protect privacy). Detailed information about the composition of the dataset can be found
here
.
Pictures from India and Singapore contributed using the Crowdsource app.
Get Involved
This is an early step on a long journey. To build inclusive ML products, training data must represent global diversity along several dimensions. To that end, we invite the global community to help expand the Open Images Extended dataset by contributing imagery from your own hometown and community. Download the
Crowdsource Android app
to contribute images you’ve taken from your phone, or
contact us
if there are other image repositories (that you have the rights for) that you’re interested in adding to open-images dataset.
Acknowledgements
The release of Open Images Extended has been possible thanks to the hard work of a lot of people including, but not limited to the following (in alphabetical order of last name): James Atwood, Pallavi Baljekar, Peggy Chi, Tulsee Doshi, Tom Duerig, Vittorio Ferrari, Akshay Gaur, Victor Gomes, Yoni Halpern, Gursheesh Kaur, Mahima Pushkarna, Jigyasa Saxena, D. Sculley, Richa Singh, Rachelle Summers.
TF-Ranking: A Scalable TensorFlow Library for Learning-to-Rank
Wednesday, December 5, 2018
Posted by Xuanhui Wang and Michael Bendersky, Software Engineers, Google AI
Ranking, the process of ordering a list of items in a way that maximizes the utility of the entire list, is applicable in a wide range of domains, from search engines and recommender systems to
machine translation
,
dialogue systems
and even
computational biology.
In applications like these (and many others), researchers often utilize a set of supervised machine learning techniques called
learning-to-rank
. In many cases, these learning-to-rank techniques are applied to datasets that are prohibitively large
—
scenarios where the scalability of
TensorFlow
could be an advantage. However, there is currently no out-of-the-box support for applying learning-to-rank techniques in TensorFlow. To the best of our knowledge, there are also no other open source libraries that specialize in applying learning-to-rank techniques at scale.
Today, we are excited to share
TF-Ranking
, a scalable TensorFlow-based library for learning-to-rank. As described in our recent
paper
, TF-Ranking provides a unified framework that includes a suite of state-of-the-art learning-to-rank algorithms, and supports
pairwise or listwise loss functions
,
multi-item scoring
,
ranking metric optimization
, and
unbiased learning-to-rank
.
TF-Ranking is fast and easy to use, and creates high-quality ranking models. The unified framework gives ML researchers, practitioners and enthusiasts the ability to evaluate and choose among an array of different ranking models within a single library. Moreover, we strongly believe that a key to a useful open source library is not only providing sensible defaults, but also empowering our users to develop their own custom models. Therefore, we provide flexible API's, within which the users can define and plug in their own customized loss functions, scoring functions and metrics.
Existing Algorithms and Metrics Support
The objective of learning-to-rank algorithms is minimizing a
loss function
defined over a list of items to optimize the utility of the list ordering for any given application. TF-Ranking supports a wide range of standard pointwise, pairwise and listwise loss functions as described in
prior work
. This ensures that researchers using the TF-Ranking library are able to reproduce and extend previously published baselines, and practitioners can make the most informed choices for their applications. Furthermore, TF-Ranking can handle sparse features (like raw text) through
embeddings
and scales to hundreds of millions of training instances. Thus, anyone who is interested in building real-world data intensive ranking systems such as web search or news recommendation, can use TF-Ranking as a robust, scalable solution.
Empirical evaluation is an important part of any machine learning or information retrieval research. To ensure compatibility with prior work, we support many of the commonly used ranking metrics, including
Mean Reciprocal Rank
(MRR) and
Normalized Discounted Cumulative Gain
(NDCG). We also make it easy to visualize these metrics at training time on
TensorBoard
, an open source TensorFlow visualization dashboard.
An example of the NDCG metric (Y-axis) along the training steps (X-axis) displayed in the TensorBoard. It shows the overall progress of the metrics during training. Different methods can be compared directly on the dashboard. Best models can be selected based on the metric.
Multi-Item Scoring
TF-Ranking supports a novel scoring mechanism wherein multiple items (e.g., web pages) can be
scored jointly
, an extension of the traditional scoring paradigm in which single items are scored independently. One challenge in multi-item scoring is the difficulty for inference where items have to be grouped and scored in subgroups. Then, scores are accumulated per-item and used for sorting. To make these complexities transparent to the user, TF-Ranking provides a List-In-List-Out (LILO) API to wrap all this logic in the exported TF models.
The TF-Ranking library supports multi-item scoring architecture, an extension of traditional single-item scoring.
As we demonstrate in
recent work
, multi-item scoring is competitive in its performance to the state-of-the-art learning-to-rank models such as RankNet, MART, and LambdaMART on a public
LETOR
benchmark.
Ranking Metric Optimization
An important research challenge in learning-to-rank is direct optimization of ranking metrics (such as the previously mentioned NDCG and MRR). These metrics, while being able to measure the performance of ranking systems better than the standard classification metrics like
Area Under the Curve
(AUC), have the unfortunate property of being either
discontinuous or flat
. Therefore standard stochastic gradient descent optimization of these metrics is problematic.
In
recent work,
we proposed a novel method, LambdaLoss, which provides a principled probabilistic framework for ranking metric optimization. In this framework, metric-driven loss functions can be designed and optimized by an
expectation-maximization
procedure. The TF-Ranking library integrates the recent advances in direct metric optimization and provides an implementation of LambdaLoss. We are hopeful that this will encourage and facilitate further research advances in the important area of ranking metric optimization.
Unbiased Learning-to-Rank
Prior research
has shown that given a ranked list of items, users are much more likely to interact with the first few results, regardless of their relevance. This observation has inspired research interest in
unbiased learning-to-rank
, and led to the development of unbiased evaluation and several unbiased learning algorithms, based on training instances re-weighting. In the TF-Ranking library, metrics are implemented to support unbiased evaluation and losses are implemented for unbiased learning by natively supporting re-weighting to overcome the inherent biases in user interactions datasets.
Getting Started with TF-Ranking
TF-Ranking implements the
TensorFlow Estimator
interface, which greatly simplifies machine learning programming by encapsulating training, evaluation, prediction and export for serving. TF-Ranking is well integrated with the rich TensorFlow ecosystem. As described above, you can use
Tensorboard
to visualize ranking metrics like NDCG and MRR, as well as to pick the best model checkpoints using these metrics. Once your model is ready, it is easy to deploy it in production using
TensorFlow Serving
.
If you’re interested in trying TF-Ranking for yourself, please check out our
GitHub repo
, and walk through the
tutorial examples
. TF-Ranking is an active research project, and we welcome your feedback and contributions. We are excited to see how TF-Ranking can help the information retrieval and machine learning research communities.
Acknowledgements
This project was only possible thanks to the members of the core TF-Ranking team: Rama Pasumarthi, Cheng Li, Sebastian Bruch, Nadav Golbandi, Stephan Wolf, Jan Pfeifer, Rohan Anil, Marc Najork, Patrick McGregor and Clemens Mewald. We thank the members of the TensorFlow team for their advice and support: Alexandre Passos, Mustafa Ispir, Karmel Allison, Martin Wicke, and others. Finally, we extend our special thanks to our collaborators, interns and early adopters: Suming Chen, Zhen Qin, Chirag Sethi, Maryam Karimzadehgan, Makoto Uchida, Yan Zhu, Qingyao Ai, Brandon Tran, Donald Metzler, Mike Colagrosso, and many others at Google who helped in evaluating and testing the early versions of TF-Ranking.
The NeurIPS 2018 Test of Time Award: The Trade-Offs of Large Scale Learning
Tuesday, December 4, 2018
Posted by Anna Ukhanova, Program Manager, Google AI Zürich
Progress in machine learning (ML) is happening so rapidly, that it can sometimes feel like any idea or algorithm more than 2 years old is already outdated or superseded by something better. However, old ideas sometimes remain relevant even when a large fraction of the scientific community has turned away from them. This is often a question of context: an idea which may seem to be a dead end in a particular context may become wildly successful in a different one. In the specific case of deep learning (DL), the growth of both the availability of data and computing power renewed interest in the area and significantly influenced research directions.
The NIPS 2007 paper “
The Trade-Offs of Large Scale Learning
” by Léon Bottou (then at
NEC Labs
, now at
Facebook AI Research
) and Olivier Bousquet (
Google AI, Zürich)
is a good example of this phenomenon. As the recent recipient of the NeurIPS 2018 Test of Time Award, this seminal work investigated the interplay between data and computation in ML, showing that if one is limited by computing power but can make use of a large dataset, it is more efficient to perform a small amount of computation on many individual training examples rather than to perform extensive computation on a subset of the data. This demonstrated the power of an old algorithm,
stochastic gradient descent
, which is nowadays used in pretty much all applications of DL.
Optimization and the Challenge of Scale
Many ML algorithms can be thought of as the combination of two main ingredients:
A model, which is a set of possible functions that will be used to fit the data.
An optimization algorithm which specifies how to find the best function in that set.
Back in the 90’s the datasets used in ML were much smaller than the ones in use today, and while artificial neural networks had already led to some successes, they were considered hard to train. In the early 2000’s, with the introduction of
Kernel Machines
(
SVMs
in particular), neural networks went out of fashion. Simultaneously, the attention shifted away from the optimization algorithms that had been used to train neural networks (stochastic gradient descent) to focus on those used for kernel machines (
quadratic programming
). One important difference being that in the former case, training examples are used one at a time to perform gradient steps (this is called “stochastic”), while in the latter case, all training examples are used at each iteration (this is called “batch”).
As the size of the training sets increased, the efficiency of optimization algorithms to handle large amounts of data became a bottleneck. For example, in the case of quadratic programming, running time scales at least quadratically in the number of examples. In other words, if you double your training set size, your training will take at least 4 times longer. Hence, lots of effort went into trying to make these algorithms scale to larger training sets (see for example
Large Scale Kernel Machines
).
People who had experience with training neural networks knew that stochastic gradient descent was comparably easier to scale to large datasets, but unfortunately its convergence is very slow (it takes lots of iterations to reach an accuracy comparable to that of a batch algorithm), so it wasn’t clear that this would be a solution to the scaling problem.
Stochastic Algorithms Scale Better
In the context of ML, the number of iterations needed to optimize the cost function is actually not the main concern: there is no point in perfectly tuning your model since you will essentially “overfit” to the training data. So why not reduce the computational effort that you put into tuning the model and instead spend the effort processing more data?
The work of Léon and Olivier provided a formal study of this phenomenon: by considering access to a large amount of data and assuming the limiting factor is computation, they showed that it is better to perform a minimal amount of computation on each individual training example (thus processing more of them) rather than performing extensive computation on a smaller amount of data.
In doing so, they also demonstrated that among various possible optimization algorithms, stochastic gradient descent is the best. This was confirmed by many experiments and led to a renewed interest in online optimization algorithms which are now in extensive use in ML.
Mysteries Remain
In the following years, many variants of stochastic gradient descent were developed both in the convex case and in the non-convex one (particularly relevant for DL). The most common variant now is the so-called “mini-batch” SGD where one considers a small number (~10-100) of training examples at each iteration, and performs several passes over the training set, with a couple of clever tricks to scale the gradient appropriately. Most ML libraries provide a default implementation of such an algorithm and it is arguably one of the pillars of DL.
While this analysis provided a solid foundation for understanding the properties of this algorithm, the amazing and sometimes surprising successes of DL continue to raise many more questions for the scientific community. In particular, the role of this algorithm in the generalization properties of deep networks has been repeatedly demonstrated but is still poorly understood. This means that a lot of fascinating questions are yet to be explored which could lead to a better understanding of the algorithms currently in use and the development of even more efficient algorithms in the future.
The perspective proposed by Léon and Olivier in their collaboration 10 years ago provided a significant boost to the development of the algorithm that is nowadays the workhorse of ML systems that benefit our lives daily, and we offer our sincere congratulations to both authors on this well-deserved award.
Google at NeurIPS 2018
Monday, December 3, 2018
Posted by Slav Petrov, Principal Scientist, Google
This week, Montréal hosts the 32
nd
annual
Conference on Neural Information Processing Systems
(NeurIPS 2018), the biggest machine learning conference of the year. The conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research. Google will have a strong presence at NeurIPS 2018, with more than 400 Googlers attending in order to contribute to, and learn from, the broader academic research community via talks, posters, workshops, competitions and tutorials. We will be presenting work that pushes the boundaries of what is possible in language understanding, translation, speech recognition and visual & audio perception, with Googlers co-authoring nearly 100 accepted papers (see below).
At the forefront of machine learning, Google is actively exploring virtually all aspects of the field spanning both theory and applications. This research is often inspired by real product needs but increasingly more often driven by scientific curiosity. Given the range of research projects that we pursue, we have found it useful to
define a new framework
which helps crystalize the goals of projects and allows us to measure progress and success in appropriate ways. Our contributions to NeurIPS and to the broader research community in general are integral to our research mission.
If you are attending NeurIPS 2018, we hope you’ll stop by our booth and chat with our researchers about the projects and opportunities at Google that go into solving the world's most challenging research problems, and to see demonstrations of some of the exciting research we pursue. You can also learn more about our work being presented in the list below (Googlers highlighted in
blue
).
Google is a Platinum Sponsor of NeurIPS 2018.
NeurIPS Foundation Board
Corinna Cortes
,
John C. Platt
,
Fernando Pereira
NeurIPS Organizing Committee
General Chair:
Samy Bengio
Program Co-Chair:
Hugo Larochelle
Party Chair:
Douglas Eck
Diversity and Inclusion Co-Chair:
Katherine A. Heller
NeurIPS Program Committee
Senior Area Chairs include:
Angela Yu
,
Claudio Gentile
,
Cordelia Schmid
,
Corinna Cortes
,
Csaba Szepesvari
,
Dale Schuurmans
,
Elad Hazan
,
Mehryar Mohri
,
Raia Hadsell
,
Satyen Kale
,
Yishay Mansour
,
Afshin Rostamizadeh
,
Alex Kulesza
Area Chairs include:
Amin Karbasi
,
Amir Globerson
,
Amit Daniely
,
Andras Gyorgy
,
Andriy Mnih
,
Been Kim
,
Branislav Kveton
,
Ce Liu, D Sculley
,
Danilo Rezende
,
Danny Tarlow
,
David Balduzzi
,
Denny Zhou
,
Dilan Gorur
,
Dumitru Erhan
,
George Dahl
,
Graham Taylor
,
Ian Goodfellow
,
Jasper Snoek
,
Jean-Philippe Vert
,
Jia Deng
,
Jon Shlens
,
Karen Simonyan
,
Kevin Swersky
,
Kun Zhang
,
Lihong Li
,
Marc G. Bellemare
,
Marco Cuturi
,
Maya Gupta
,
Michael Bowling
,
Michalis Titsias
,
Mohammad Norouzi
,
Mouhamadou Moustapha Cisse
,
Nicolas Le Roux
,
Remi Munos
,
Sanjiv Kumar
,
Sanmi Koyejo
,
Sergey Levine
,
Silvia Chiappa, Slav Petrov
,
Surya Ganguli
,
Timnit Gebru
,
Timothy Lillicrap
,
Viren Jain
,
Vitaly Feldman
,
Vitaly Kuznetsov
Workshops Program Committee includes:
Mehryar Mohri, Sergey Levine
Accepted Papers
3D-Aware Scene Manipulation via Inverse Graphics
Shunyu Yao, Tzu Ming Harry Hsu, Jun-Yan Zhu, Jiajun Wu, Antonio Torralba,
William T. Freeman
, Joshua B. Tenenbaum
A Retrieve-and-Edit Framework for Predicting Structured Outputs
Tatsunori Hashimoto,
Kelvin Guu
, Yonatan Oren, Percy Liang
Adversarial Attacks on Stochastic Bandits
Kwang-Sung Jun,
Lihong Li
, Yuzhe Ma, Xiaojin Zhu
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
Gamaleldin F. Elsayed
, Shreya Shankar, Brian Cheung, Nicolas Papernot,
Alex Kurakin
,
Ian Goodfellow
,
Jascha Sohl-Dickstein
Adversarially Robust Generalization Requires More Data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras,
Kunal Talwar
, Aleksander Madry
Are GANs Created Equal? A Large-Scale Study
Mario Lucic
,
Karol Kurach
,
Marcin Michalski
,
Olivier Bousquet
,
Sylvain Gelly
Collaborative Learning for Deep Neural Networks
Guocong Song,
Wei Chai
Completing State Representations using Spectral Learning
Nan Jiang,
Alex Kulesza
, Santinder Singh
Content Preserving Text Generation with Attribute Controls
Lajanugen Logeswaran,
Honglak Lee, Samy Bengio
Context-aware Synthesis and Placement of Object Instances
Donghoon Lee, Sifei Liu, Jinwei Gu, Ming-Yu Liu,
Ming-Hsuan Yang
, Jan Kautz
Co-regularized Alignment for Unsupervised Domain Adaptation
Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerlo Feris,
William T. Freeman
, Gregory Wornell
cpSGD: Communication-efficient and differentially-private distributed SGD
Naman Agarwal,
Ananda Theertha Suresh, Felix Yu, Sanjiv Kumar, H. Brendan Mcmahan
Data Center Cooling Using Model-Predictive Control
Nevena Lazic
,
Craig Boutilier
,
Tyler Lu
,
Eehern Wong
,
Binz Roy
,
MK Ryu
,
Greg Imwalle
Data-Efficient Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine
Deep Attentive Tracking via Reciprocative Learning
Shi Pu, Yibing Song, Chao Ma, Honggang Zhang,
Ming-Hsuan Yang
Generalizing Point Embeddings Using the Wasserstein Space of Elliptical Distributions
Boris Muzellec,
Marco Cuturi
GLoMo: Unsupervised Learning of Transferable Relational Graphs
Zhilin Yang, Jake (Junbo) Zhao, Bhuwan Dhingra, Kaiming He,
William W. Cohen
, Ruslan Salakhutdinov, Yann LeCun
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking
Patrick Chen
, Si Si, Yang Li, Ciprian Chelba, Cho-Jui Hsieh
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections
Xin Zhang, Armando Solar-Lezama,
Rishabh Singh
Learning Hierarchical Semantic Image Manipulation through Structured Representations
Seunghoon Hong, Xinchen Yan, Thomas Huang,
Honglak Lee
Learning Temporal Point Processes via Reinforcement Learning
Shuang Li, Shuai Xiao, Shixiang Zhu,
Nan Du
, Yao Xie, Le Song
Learning Towards Minimum Hyperspherical Energy
Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu,
Bo Dai
, Le Song
Mesh-TensorFlow: Deep Learning for Supercomputers
Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake Hechtman
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
Edward Choi
, Cao Xiao, Walter F. Stewart, Jimeng Sun
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
Liang-Chieh Chen, Maxwell D. Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, Jonathon Shlens
SplineNets: Continuous Neural Decision Graphs
Cem Keskin, Shahram Izadi
Task-Driven Convolutional Recurrent Models of the Visual System
Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar,
Surya Ganguli, David Sussillo
, James J. DiCarlo, Daniel L. K. Yamins
To Trust or Not to Trust a Classifier
Heinrich Jiang, Been Kim
, Melody Guan,
Maya Gupta
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Ye Jia, Yu Zhang, Ron J. Weiss, Quan Wang, Jonathan Shen, Fei Ren, Zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio Lopez Moreno, Yonghui Wu
Algorithms and Theory for Multiple-Source Adaptation
Judy Hoffman,
Mehryar Mohri
, Ningshan Zhang
A Lyapunov-based Approach to Safe Reinforcement Learning
Yinlam Chow,
Ofir Nachum
, Edgar Duenez-Guzman, Mohammad Ghavamzadeh
Adaptive Methods for Nonconvex Optimization
Manzil Zaheer, Sashank Reddi
, Devendra Sachan,
Satyen Kale, Sanjiv Kumar
Assessing Generative Models via Precision and Recall
Mehdi S. M. Sajjadi,
Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly
A Loss Framework for Calibrated Anomaly Detection
Aditya Menon
, Robert Williamson
Blockwise Parallel Decoding for Deep Autoregressive Models
Mitchell Stern, Noam Shazeer, Jakob Uszkoreit
Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
Qiang Liu,
Lihong Li
, Ziyang Tang,
Dengyong Zhou
Contextual Pricing for Lipschitz Buyers
Jieming Mao,
Renato Leme
, Jon Schneider
Coupled Variational Bayes via Optimization Embedding
Bo Dai
, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
Data Amplification: A Unified and Competitive Approach to Property Estimation
Yi HAO, Alon Orlitsky,
Ananda Theertha Suresh
, Yihong Wu
Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images
Elisabeta Marinoiu, Mihai Zanfir, Alin-Ionut Popa,
Cristian Sminchisescu
Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation
Wenqi Ren, Jiawei Zhang, Lin Ma, Jinshan Pan, Xiaochun Cao, Wei Liu,
Ming-Hsuan Yang
Diminishing Returns Shape Constraints for Interpretability and Regularization
Maya Gupta, Dara Bahri, Andrew Cotter, Kevin Canini
DropBlock: A Regularization Method for Convolutional Networks
Golnaz Ghiasi, Tsung-Yi Lin,
Quoc V. Le
Generalization Bounds for Uniformly Stable Algorithms
Vitaly Feldman
, Jan Vondrak
Geometrically Coupled Monte Carlo Sampling
Mark Rowland,
Krzysztof Choromanski
, Francois Chalus, Aldo Pacchiano,
Tamas Sarlos
, Richard E. Turner, Adrian Weller
GILBO: One Metric to Measure Them All
Alexander A. Alemi,
Ian Fischer
Insights on Representational Similarity in Neural Networks with Canonical Correlation
Ari S. Morcos,
Maithra Raghu, Samy Bengio
Improving Online Algorithms via ML Predictions
Manish Purohit, Zoya Svitkina, Ravi Kumar
Learning to Exploit Stability for 3D Scene Parsing
Yilun Du, Zhijan Liu, Hector Basevi, Ales Leonardis,
William T. Freeman
, Josh Tenembaum, Jiajun Wu
Maximizing Induced Cardinality Under a Determinantal Point Process
Jennifer Gillenwater, Alex Kulesza, Sergei Vassilvitskii
, Zelda Mariet
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
Chen Liang, Mohammad Norouzi
, Jonathan Berant,
Quoc V. Le
, Ni Lao
PCA of High Dimensional Random Walks with Comparison to Neural Network Training
Joseph M. Antognini, Jascha Sohl-Dickstein
Predictive Approximate Bayesian Computation via Saddle Points
Yingxiang Yang,
Bo Dai
, Negar Kiyavash, Niao He
Recurrent World Models Facilitate Policy Evolution
David Ha
, Jürgen Schmidhuber
Sanity Checks for Saliency Maps
Julius Adebayo,
Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, Been Kim
Simple, Distributed, and Accelerated Probabilistic Programming
Dustin Tran, Matthew Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew Johnson, Rif A. Saurous
Tangent: Automatic Differentiation Using Source-Code Transformation for Dynamically Typed Array Programming
Bart van Merriënboer, Dan Moldovan, Alex Wiltschko
The Emergence of Multiple Retinal Cell Types Through Efficient Coding of Natural Movies
Samuel A. Ocko, Jack Lindsey,
Surya Ganguli
, Stephane Deny
The Everlasting Database: Statistical Validity at a Fair Price
Blake Woodworth,
Vitaly Feldman
, Saharon Rosset, Nathan Srebro
The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network
Jeffrey Pennington, Pratik Worah
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee, Kibok Lee,
Honglak Lee
, Jinwoo Shin
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
Matthew D. Hoffman, Matthew Johnson, Dustin Tran
A Bayesian Nonparametric View on Count-Min Sketch
Diana Cai, Michael Mitzenmacher,
Ryan Adams
(no longer at Google)
Automatic Differentiation in ML: Where We are and Where We Should be Going
Bart van Merriënboer
, Olivier Breuleux, Arnaud Bergeron,
Pascal Lamblin
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
Sergey Bartunov, Adam Santoro, Blake A. Richards,
Geoffrey E. Hinton
, Timothy P. Lillicrap
Deep Generative Models for Distribution-Preserving Lossy Compression
Michael Tschannen,
Eirikur Agustsson, Mario Lucic
Deep Structured Prediction with Nonlinear Output Transformations
Colin Graber,
Ofer Meshi
, Alexander Schwing
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
Supasorn Suwajanakorn, Noah Snavely, Jonathan Tompson, Mohammad Norouzi
Transfer Learning with Neural AutoML
Catherine Wong,
Neil Houlsby, Yifeng Lu, Andrea Gesmundo
Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Dmitry Storcheus
, Scott Yang
Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai,
Bo Dai
, Srinivas Aluru
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
Blake Woodworth, Jialei Wang,
Brendan McMahan
, Nathan Srebro
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies
Sungryull Sohn, Junhyuk Oh,
Honglak Lee
Human-in-the-Loop Interpretability Prior
Isaac Lage, Andrew Slavin Ross,
Been Kim
, Samuel J. Gershman, Finale Doshi-Velez
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
David Minnen, Johannes Ballé, George D Toderici
Large-Scale Computation of Means and Clusters for Persistence Diagrams Using Optimal Transport
Théo Lacombe, Steve Oudot,
Marco Cuturi
Learning to Reconstruct Shapes from Unseen Classes
Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Joshua B. Tenenbaum,
William T. Freeman
, Jiajun Wu
Large Margin Deep Networks for Classification
Gamaleldin Fathy Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, Samy Bengio
Mallows Models for Top-k Lists
Flavio Chierichetti, Anirban Dasgupta, Shahrzad Haddadan,
Ravi Kumar, Silvio Lattanzi
Meta-Learning MCMC Proposals
Tongzhou Wang, YI WU,
Dave Moore
, Stuart Russell
Non-delusional Q-Learning and Value-Iteration
Tyler Lu, Dale Schuurmans, Craig Boutilier
Online Learning of Quantum States
Scott Aaronson, Xinyi Chen,
Elad Hazan, Satyen Kale
, Ashwin Nayak
Online Reciprocal Recommendation with Theoretical Performance Guarantees
Fabio Vitale, Nikos Parotsidis,
Claudio Gentile
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
Rad Niazadeh, Tim Roughgarden,
Joshua R. Wang
Policy Regret in Repeated Games
Raman Arora, Michael Dinitz, Teodor Vanislavov Marinov,
Mehryar Mohri
Provable Variational Inference for Constrained Log-Submodular Models
Josip Djolonga
, Stefanie Jegelka, Andreas Krause
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Jacob Buckman
, Danijar Hafner,
George Tucker, Eugene Brevdo, Honglak Lee
Visual Object Networks: Image Generation with Disentangled 3D Representations
JunYan Zhu, Zhoutong Zhang, Chengkai Zhang, Jiajun Wu, Antonio Torralba, Josh Tenenbaum,
William T. Freeman
Watch Your Step: Learning Node Embeddings via Graph Attention
Sami Abu-El-Haija,
Bryan Perozzi, Rami AlRfou, Alexander Alemi
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Bayesian Deep Learning
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Continual Learning
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Visually Grounded Interaction and Language
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AI for Social Good
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Black in AI
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LatinX in AI
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Pablo Samuel Castro
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Machine Learning for Systems
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,
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Queer in AI
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esse Engel, Adam Roberts
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