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Google LLC is an American multinational technology company that specializes in Internet-related services and products, which include online advertising technologies, search engine, cloud computing, software, and hardware.

Featured Researchers

Sergey Levine
177 publications
Wei Wang
160 publications
Bo Li
139 publications
Yu Zhang
124 publications
Wei Li
100 publications
Li Fei-Fei
96 publications
Andrew Zisserman
93 publications
Yee Whye Teh
82 publications
Pushmeet Kohli
77 publications
Xin Li
75 publications
Oriol Vinyals
71 publications

Bayesian Optimization in AlphaGo

During the development of AlphaGo, its many hyper-parameters were tuned ...
12/17/2018 ∙ by Yutian Chen, et al. ∙ 128 ∙ share

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DermGAN: Synthetic Generation of Clinical Skin Images with Pathology

Despite the recent success in applying supervised deep learning to medic...
11/20/2019 ∙ by Amirata Ghorbani, et al. ∙ 66 ∙ share

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The Hanabi Challenge: A New Frontier for AI Research

From the early days of computing, games have been important testbeds for...
02/01/2019 ∙ by Nolan Bard, et al. ∙ 54 ∙ share

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Disentangling trainability and generalization in deep learning

A fundamental goal in deep learning is the characterization of trainabil...
12/30/2019 ∙ by Lechao Xiao, et al. ∙ 54 ∙ share

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Searching for MobileNetV3

We present the next generation of MobileNets based on a combination of c...
05/06/2019 ∙ by Andrew Howard, et al. ∙ 53 ∙ share

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Single Image Portrait Relighting

Lighting plays a central role in conveying the essence and depth of the ...
05/02/2019 ∙ by Tiancheng Sun, et al. ∙ 50 ∙ share

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DeepRED: Deep Image Prior Powered by RED

Inverse problems in imaging are extensively studied, with a variety of s...
03/25/2019 ∙ by Gary Mataev, et al. ∙ 48 ∙ share

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Making Sense of Reinforcement Learning and Probabilistic Inference

Reinforcement learning (RL) combines a control problem with statistical ...
01/03/2020 ∙ by Brendan O'Donoghue, et al. ∙ 46 ∙ share

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Flow Contrastive Estimation of Energy-Based Models

This paper studies a training method to jointly estimate an energy-based...
12/02/2019 ∙ by Ruiqi Gao, et al. ∙ 42 ∙ share

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Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models

We introduce a new local sparse attention layer that preserves two-dimen...
11/27/2019 ∙ by Giannis Daras, et al. ∙ 41 ∙ share

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Automating Interpretability: Discovering and Testing Visual Concepts Learned by Neural Networks

Interpretability has become an important topic of research as more machi...
02/07/2019 ∙ by Amirata Ghorbani, et al. ∙ 40 ∙ share

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SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks

Deep networks have achieved excellent results in perceptual tasks, yet t...
12/25/2019 ∙ by Alex Lamb, et al. ∙ 40 ∙ share

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Low-Weight and Learnable Image Denoising

Image denoising is a well studied problem with an extensive activity tha...
11/17/2019 ∙ by Gregory Vaksman, et al. ∙ 39 ∙ share

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AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework ...
04/30/2019 ∙ by Charles Weill, et al. ∙ 38 ∙ share

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Bayesian Inference for Large Scale Image Classification

Bayesian inference promises to ground and improve the performance of dee...
08/09/2019 ∙ by Jonathan Heek, et al. ∙ 38 ∙ share

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Self-training with Noisy Student improves ImageNet classification

We present a simple self-training method that achieves 87.4 on ImageNet,...
11/11/2019 ∙ by Qizhe Xie, et al. ∙ 36 ∙ share

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In-domain representation learning for remote sensing

Given the importance of remote sensing, surprisingly little attention ha...
11/15/2019 ∙ by Maxim Neumann, et al. ∙ 36 ∙ share

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Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks

Natural gradient descent has proven effective at mitigating the effects ...
05/27/2019 ∙ by Guodong Zhang, et al. ∙ 35 ∙ share

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End-to-End Learning of Visual Representations from Uncurated Instructional Videos

Annotating videos is cumbersome, expensive and not scalable. Yet, many s...
12/13/2019 ∙ by Antoine Miech, et al. ∙ 35 ∙ share

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Mathematical Reasoning in Latent Space

We design and conduct a simple experiment to study whether neural networ...
09/26/2019 ∙ by Dennis Lee, et al. ∙ 34 ∙ share

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Extending Machine Language Models toward Human-Level Language Understanding

Language is central to human intelligence. We review recent breakthrough...
12/12/2019 ∙ by James L. McClelland, et al. ∙ 34 ∙ share

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A Generalized Framework for Population Based Training

Population Based Training (PBT) is a recent approach that jointly optimi...
02/05/2019 ∙ by Ang Li, et al. ∙ 32 ∙ share

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Adaptive Online Planning for Continual Lifelong Learning

We study learning control in an online lifelong learning scenario, where...
12/03/2019 ∙ by Kevin Lu, et al. ∙ 31 ∙ share

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Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...
12/10/2019 ∙ by Peter Kairouz, et al. ∙ 31 ∙ share

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The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction

This paper studies the problem of predicting the distribution over multi...
12/13/2019 ∙ by Junwei Liang, et al. ∙ 31 ∙ share

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Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

Learning to predict scene depth from RGB inputs is a challenging task bo...
11/15/2018 ∙ by Vincent Casser, et al. ∙ 30 ∙ share

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Learning to Walk via Deep Reinforcement Learning

Deep reinforcement learning suggests the promise of fully automated lear...
12/26/2018 ∙ by Tuomas Haarnoja, et al. ∙ 30 ∙ share

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On the Convergence of Adam and Beyond

Several recently proposed stochastic optimization methods that have been...
04/19/2019 ∙ by Sashank J Reddi, et al. ∙ 30 ∙ share

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3D Conditional Generative Adversarial Networks to enable large-scale seismic image enhancement

We propose GAN-based image enhancement models for frequency enhancement ...
11/16/2019 ∙ by Praneet Dutta, et al. ∙ 30 ∙ share

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Adaptive Scheduling for Multi-Task Learning

To train neural machine translation models simultaneously on multiple ta...
09/13/2019 ∙ by Sébastien Jean, et al. ∙ 29 ∙ share

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CAQL: Continuous Action Q-Learning

Value-based reinforcement learning (RL) methods like Q-learning have sho...
09/26/2019 ∙ by Moonkyung Ryu, et al. ∙ 29 ∙ share

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Generative Models for Effective ML on Private, Decentralized Datasets

To improve real-world applications of machine learning, experienced mode...
11/15/2019 ∙ by Sean Augenstein, et al. ∙ 29 ∙ share

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NoRML: No-Reward Meta Learning

Efficiently adapting to new environments and changes in dynamics is crit...
03/04/2019 ∙ by Yuxiang Yang, et al. ∙ 28 ∙ share

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Exponential Family Estimation via Adversarial Dynamics Embedding

We present an efficient algorithm for maximum likelihood estimation (MLE...
04/27/2019 ∙ by Bo Dai, et al. ∙ 28 ∙ share

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A Deep Factorization of Style and Structure in Fonts

We propose a deep factorization model for typographic analysis that dise...
10/02/2019 ∙ by Akshay Srivatsan, et al. ∙ 28 ∙ share

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Behavior Regularized Offline Reinforcement Learning

In reinforcement learning (RL) research, it is common to assume access t...
11/26/2019 ∙ by Yifan Wu, et al. ∙ 28 ∙ share

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An Analysis of Object Representations in Deep Visual Trackers

Fully convolutional deep correlation networks are integral components of...
01/08/2020 ∙ by Ross Goroshin, et al. ∙ 28 ∙ share

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Identity Crisis: Memorization and Generalization under Extreme Overparameterization

We study the interplay between memorization and generalization of overpa...
02/13/2019 ∙ by Chiyuan Zhang, et al. ∙ 26 ∙ share

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Meta-learners' learning dynamics are unlike learners'

Meta-learning is a tool that allows us to build sample-efficient learnin...
05/03/2019 ∙ by Neil C. Rabinowitz, et al. ∙ 26 ∙ share

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Learning Sparse Networks Using Targeted Dropout

Neural networks are easier to optimise when they have many more weights ...
05/31/2019 ∙ by Aidan N. Gomez, et al. ∙ 26 ∙ share

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Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model

Multilingual end-to-end (E2E) models have shown great promise in expansi...
09/11/2019 ∙ by Anjuli Kannan, et al. ∙ 26 ∙ share

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RandAugment: Practical data augmentation with no separate search

Recent work has shown that data augmentation has the potential to signif...
09/30/2019 ∙ by Ekin D. Cubuk, et al. ∙ 26 ∙ share

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What Can Learned Intrinsic Rewards Capture?

Reinforcement learning agents can include different components, such as ...
12/11/2019 ∙ by Zeyu Zheng, et al. ∙ 25 ∙ share

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Unifying Deep Local and Global Features for Efficient Image Search

A key challenge in large-scale image retrieval problems is the trade-off...
01/14/2020 ∙ by Bingyi Cao, et al. ∙ 25 ∙ share

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AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

Recurrent neural networks have gained widespread use in modeling sequent...
02/26/2019 ∙ by Bo Chang, et al. ∙ 24 ∙ share

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ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

We improve the recently-proposed "MixMatch" semi-supervised learning alg...
11/21/2019 ∙ by David Berthelot, et al. ∙ 24 ∙ share

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Are Transformers universal approximators of sequence-to-sequence functions?

Despite the widespread adoption of Transformer models for NLP tasks, the...
12/20/2019 ∙ by Chulhee Yun, et al. ∙ 24 ∙ share

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Meta-Learning surrogate models for sequential decision making

Meta-learning methods leverage past experience to learn data-driven indu...
03/28/2019 ∙ by Alexandre Galashov, et al. ∙ 22 ∙ share

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AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Modern deep neural networks can achieve high accuracy when the training ...
12/05/2019 ∙ by Dan Hendrycks, et al. ∙ 21 ∙ share

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Observational Overfitting in Reinforcement Learning

A major component of overfitting in model-free reinforcement learning (R...
12/06/2019 ∙ by Xingyou Song, et al. ∙ 21 ∙ share

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