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research

∙ 12/17/2018

Bayesian Optimization in AlphaGo

During the development of AlphaGo, its many hyper-parameters were tuned ...

Yutian Chen, et al. ∙

research

∙ 01/28/2021

Machine learning accelerated computational fluid dynamics

Numerical simulation of fluids plays an essential role in modeling many ...

Dmitrii Kochkov, et al. ∙

research

∙ 06/29/2020

Self-Supervised MultiModal Versatile Networks

Videos are a rich source of multi-modal supervision. In this work, we le...

Jean-Baptiste Alayrac, et al. ∙

research

∙ 12/21/2020

RealFormer: Transformer Likes Residual Attention

Transformer is the backbone of modern NLP models. In this paper, we prop...

Ruining He, et al. ∙

research

∙ 02/27/2020

Using a thousand optimization tasks to learn hyperparameter search strategies

We present TaskSet, a dataset of tasks for use in training and evaluatin...

Luke Metz, et al. ∙

research

∙ 11/20/2019

DermGAN: Synthetic Generation of Clinical Skin Images with Pathology

Despite the recent success in applying supervised deep learning to medic...

Amirata Ghorbani, et al. ∙

research

∙ 07/20/2021

WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset

We present a new dataset of Wikipedia articles each paired with a knowle...

Luyu Wang, et al. ∙

research

∙ 06/12/2020

Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC

Learning energy-based model (EBM) requires MCMC sampling of the learned ...

Erik Nijkamp, et al. ∙

research

∙ 01/22/2020

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

Uncertainty quantification for deep learning is a challenging open probl...

Nicolas Brosse, et al. ∙

research

∙ 02/01/2019

The Hanabi Challenge: A New Frontier for AI Research

From the early days of computing, games have been important testbeds for...

Nolan Bard, et al. ∙

research

∙ 12/30/2019

Disentangling trainability and generalization in deep learning

A fundamental goal in deep learning is the characterization of trainabil...

Lechao Xiao, et al. ∙

research

∙ 05/18/2022

Meta-Learning Sparse Compression Networks

Recent work in Deep Learning has re-imagined the representation of data ...

Jonathan Richard Schwarz, et al. ∙

research

∙ 05/06/2019

Searching for MobileNetV3

We present the next generation of MobileNets based on a combination of c...

Andrew Howard, et al. ∙

research

∙ 03/04/2020

Creating High Resolution Images with a Latent Adversarial Generator

Generating realistic images is difficult, and many formulations for this...

David Berthelot, et al. ∙

research

∙ 07/27/2020

Towards Learning Convolutions from Scratch

Convolution is one of the most essential components of architectures use...

Behnam Neyshabur, et al. ∙

research

∙ 03/29/2022

Learning neural audio features without supervision

Deep audio classification, traditionally cast as training a deep neural ...

Sarthak Yadav, et al. ∙

research

∙ 05/02/2019

Single Image Portrait Relighting

Lighting plays a central role in conveying the essence and depth of the ...

Tiancheng Sun, et al. ∙

research

∙ 03/25/2019

DeepRED: Deep Image Prior Powered by RED

Inverse problems in imaging are extensively studied, with a variety of s...

Gary Mataev, et al. ∙

research

∙ 02/04/2021

How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned

Deep reinforcement learning (RL) has emerged as a promising approach for...

Julian Ibarz, et al. ∙

research

∙ 10/08/2021

A Loss Curvature Perspective on Training Instability in Deep Learning

In this work, we study the evolution of the loss Hessian across many cla...

Justin Gilmer, et al. ∙

research

∙ 01/03/2020

Making Sense of Reinforcement Learning and Probabilistic Inference

Reinforcement learning (RL) combines a control problem with statistical ...

Brendan O'Donoghue, et al. ∙

research

∙ 04/17/2020

ETC: Encoding Long and Structured Data in Transformers

Transformer-based models have pushed the state of the art in many natura...

Joshua Ainslie, et al. ∙

research

∙ 10/13/2021

Dropout Prediction Variation Estimation Using Neuron Activation Strength

It is well-known DNNs would generate different prediction results even g...

Haichao Yu, et al. ∙

research

∙ 10/26/2021

Hierarchical Transformers Are More Efficient Language Models

Transformer models yield impressive results on many NLP and sequence mod...

Piotr Nawrot, et al. ∙

research

∙ 02/22/2021

Towards Causal Representation Learning

The two fields of machine learning and graphical causality arose and dev...

Bernhard Schölkopf, et al. ∙

research

∙ 12/02/2019

Flow Contrastive Estimation of Energy-Based Models

This paper studies a training method to jointly estimate an energy-based...

Ruiqi Gao, et al. ∙

research

∙ 01/14/2022

When less is more: Simplifying inputs aids neural network understanding

How do neural network image classifiers respond to simpler and simpler i...

Robin Tibor Schirrmeister, et al. ∙

research

∙ 11/27/2019

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

We introduce a new local sparse attention layer that preserves two-dimen...

Giannis Daras, et al. ∙

research

∙ 05/21/2020

Why distillation helps: a statistical perspective

Knowledge distillation is a technique for improving the performance of a...

Aditya Krishna Menon, et al. ∙

research

∙ 02/07/2019

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

Interpretability has become an important topic of research as more machi...

Amirata Ghorbani, et al. ∙

research

∙ 11/17/2019

Low-Weight and Learnable Image Denoising

Image denoising is a well studied problem with an extensive activity tha...

Gregory Vaksman, et al. ∙

research

∙ 12/25/2019

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...

Alex Lamb, et al. ∙

research

∙ 10/06/2020

Global Self-Attention Networks for Image Recognition

Recently, a series of works in computer vision have shown promising resu...

Shen Zhuoran, et al. ∙

research

∙ 04/30/2019

AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework ...

Charles Weill, et al. ∙

research

∙ 08/09/2019

Bayesian Inference for Large Scale Image Classification

Bayesian inference promises to ground and improve the performance of dee...

Jonathan Heek, et al. ∙

research

∙ 07/27/2020

Learning Compositional Neural Programs for Continuous Control

We propose a novel solution to challenging sparse-reward, continuous con...

Thomas Pierrot, et al. ∙

research

∙ 11/11/2019

Self-training with Noisy Student improves ImageNet classification

We present a simple self-training method that achieves 87.4 on ImageNet,...

Qizhe Xie, et al. ∙

research

∙ 11/15/2019

In-domain representation learning for remote sensing

Given the importance of remote sensing, surprisingly little attention ha...

Maxim Neumann, et al. ∙

research

∙ 05/27/2019

Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks

Natural gradient descent has proven effective at mitigating the effects ...

Guodong Zhang, et al. ∙

research

∙ 12/13/2019

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

Annotating videos is cumbersome, expensive and not scalable. Yet, many s...

Antoine Miech, et al. ∙

research

∙ 07/24/2020

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...

Rui Huang, et al. ∙

research

∙ 09/26/2019

Mathematical Reasoning in Latent Space

We design and conduct a simple experiment to study whether neural networ...

Dennis Lee, et al. ∙

research

∙ 12/12/2019

Extending Machine Language Models toward Human-Level Language Understanding

Language is central to human intelligence. We review recent breakthrough...

James L. McClelland, et al. ∙

research

∙ 11/10/2021

Palette: Image-to-Image Diffusion Models

We introduce Palette, a simple and general framework for image-to-image ...

Chitwan Saharia, et al. ∙

research

∙ 12/10/2019

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...

Peter Kairouz, et al. ∙

research

∙ 06/11/2020

Pointer Graph Networks

Graph neural networks (GNNs) are typically applied to static graphs that...

Petar Veličković, et al. ∙

research

∙ 11/29/2021

Collective Intelligence for Deep Learning: A Survey of Recent Developments

In the past decade, we have witnessed the rise of deep learning to domin...

David Ha, et al. ∙

research

∙ 02/05/2019

A Generalized Framework for Population Based Training

Population Based Training (PBT) is a recent approach that jointly optimi...

Ang Li, et al. ∙

research

∙ 12/13/2019

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

This paper studies the problem of predicting the distribution over multi...

Junwei Liang, et al. ∙

research

∙ 02/25/2020

Three Approaches for Personalization with Applications to Federated Learning

The standard objective in machine learning is to train a single model fo...

Yishay Mansour, et al. ∙

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