Google

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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

Wei Wang
279 publications
Sergey Levine
252 publications
Bo Li
226 publications
Yu Zhang
222 publications
Wei Li
152 publications
Xin Li
144 publications
Andrew Zisserman
131 publications
Li Zhang
130 publications
Ting Liu
126 publications
Li Fei-Fei
107 publications
Cho-Jui Hsieh
99 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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Machine learning accelerated computational fluid dynamics

Numerical simulation of fluids plays an essential role in modeling many ...
01/28/2021 ∙ by Dmitrii Kochkov, et al. ∙ 86 ∙ share

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Self-Supervised MultiModal Versatile Networks

Videos are a rich source of multi-modal supervision. In this work, we le...
06/29/2020 ∙ by Jean-Baptiste Alayrac, et al. ∙ 82 ∙ share

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RealFormer: Transformer Likes Residual Attention

Transformer is the backbone of modern NLP models. In this paper, we prop...
12/21/2020 ∙ by Ruining He, et al. ∙ 74 ∙ share

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Using a thousand optimization tasks to learn hyperparameter search strategies

We present TaskSet, a dataset of tasks for use in training and evaluatin...
02/27/2020 ∙ by Luke Metz, et al. ∙ 71 ∙ 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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Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC

Learning energy-based model (EBM) requires MCMC sampling of the learned ...
06/12/2020 ∙ by Erik Nijkamp, et al. ∙ 61 ∙ share

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On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation

Uncertainty quantification for deep learning is a challenging open probl...
01/22/2020 ∙ by Nicolas Brosse, et al. ∙ 60 ∙ 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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Creating High Resolution Images with a Latent Adversarial Generator

Generating realistic images is difficult, and many formulations for this...
03/04/2020 ∙ by David Berthelot, et al. ∙ 53 ∙ share

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Towards Learning Convolutions from Scratch

Convolution is one of the most essential components of architectures use...
07/27/2020 ∙ by Behnam Neyshabur, 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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How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned

Deep reinforcement learning (RL) has emerged as a promising approach for...
02/04/2021 ∙ by Julian Ibarz, 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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ETC: Encoding Long and Structured Data in Transformers

Transformer-based models have pushed the state of the art in many natura...
04/17/2020 ∙ by Joshua Ainslie, et al. ∙ 46 ∙ share

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Towards Causal Representation Learning

The two fields of machine learning and graphical causality arose and dev...
02/22/2021 ∙ by Bernhard Schölkopf, et al. ∙ 44 ∙ 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. ∙ 43 ∙ 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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Why distillation helps: a statistical perspective

Knowledge distillation is a technique for improving the performance of a...
05/21/2020 ∙ by Aditya Krishna Menon, 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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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. ∙ 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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Global Self-Attention Networks for Image Recognition

Recently, a series of works in computer vision have shown promising resu...
10/06/2020 ∙ by Shen Zhuoran, et al. ∙ 40 ∙ 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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Learning Compositional Neural Programs for Continuous Control

We propose a novel solution to challenging sparse-reward, continuous con...
07/27/2020 ∙ by Thomas Pierrot, 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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An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds

Detecting objects in 3D LiDAR data is a core technology for autonomous d...
07/24/2020 ∙ by Rui Huang, 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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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. ∙ 33 ∙ share

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Pointer Graph Networks

Graph neural networks (GNNs) are typically applied to static graphs that...
06/11/2020 ∙ by Petar Veličković, et al. ∙ 33 ∙ 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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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. ∙ 32 ∙ share

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Three Approaches for Personalization with Applications to Federated Learning

The standard objective in machine learning is to train a single model fo...
02/25/2020 ∙ by Yishay Mansour, 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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Uncertainty Estimation with Infinitesimal Jackknife, Its Distribution and Mean-Field Approximation

Uncertainty quantification is an important research area in machine lear...
06/13/2020 ∙ by Zhiyun Lu, 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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