
Bayesian Optimization in AlphaGo
During the development of AlphaGo, its many hyperparameters were tuned ...
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SelfSupervised MultiModal Versatile Networks
Videos are a rich source of multimodal supervision. In this work, we le...
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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...
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DermGAN: Synthetic Generation of Clinical Skin Images with Pathology
Despite the recent success in applying supervised deep learning to medic...
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Learning Energybased Model with Flowbased Backbone by Neural Transport MCMC
Learning energybased model (EBM) requires MCMC sampling of the learned ...
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On LastLayer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation
Uncertainty quantification for deep learning is a challenging open probl...
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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...
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Disentangling trainability and generalization in deep learning
A fundamental goal in deep learning is the characterization of trainabil...
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Searching for MobileNetV3
We present the next generation of MobileNets based on a combination of c...
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Creating High Resolution Images with a Latent Adversarial Generator
Generating realistic images is difficult, and many formulations for this...
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Towards Learning Convolutions from Scratch
Convolution is one of the most essential components of architectures use...
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Single Image Portrait Relighting
Lighting plays a central role in conveying the essence and depth of the ...
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DeepRED: Deep Image Prior Powered by RED
Inverse problems in imaging are extensively studied, with a variety of s...
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Making Sense of Reinforcement Learning and Probabilistic Inference
Reinforcement learning (RL) combines a control problem with statistical ...
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ETC: Encoding Long and Structured Data in Transformers
Transformerbased models have pushed the state of the art in many natura...
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Flow Contrastive Estimation of EnergyBased Models
This paper studies a training method to jointly estimate an energybased...
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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 twodimen...
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Why distillation helps: a statistical perspective
Knowledge distillation is a technique for improving the performance of a...
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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...
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LowWeight and Learnable Image Denoising
Image denoising is a well studied problem with an extensive activity tha...
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SketchTransfer: A Challenging New Task for Exploring DetailInvariance and the Abstractions Learned by Deep Networks
Deep networks have achieved excellent results in perceptual tasks, yet t...
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AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles
AdaNet is a lightweight TensorFlowbased (Abadi et al., 2015) framework ...
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Bayesian Inference for Large Scale Image Classification
Bayesian inference promises to ground and improve the performance of dee...
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Learning Compositional Neural Programs for Continuous Control
We propose a novel solution to challenging sparsereward, continuous con...
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Selftraining with Noisy Student improves ImageNet classification
We present a simple selftraining method that achieves 87.4 on ImageNet,...
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Indomain representation learning for remote sensing
Given the importance of remote sensing, surprisingly little attention ha...
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Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks
Natural gradient descent has proven effective at mitigating the effects ...
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EndtoEnd Learning of Visual Representations from Uncurated Instructional Videos
Annotating videos is cumbersome, expensive and not scalable. Yet, many s...
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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...
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Mathematical Reasoning in Latent Space
We design and conduct a simple experiment to study whether neural networ...
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Extending Machine Language Models toward HumanLevel Language Understanding
Language is central to human intelligence. We review recent breakthrough...
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Pointer Graph Networks
Graph neural networks (GNNs) are typically applied to static graphs that...
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A Generalized Framework for Population Based Training
Population Based Training (PBT) is a recent approach that jointly optimi...
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Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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The Garden of Forking Paths: Towards MultiFuture Trajectory Prediction
This paper studies the problem of predicting the distribution over multi...
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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...
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Adaptive Online Planning for Continual Lifelong Learning
We study learning control in an online lifelong learning scenario, where...
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Uncertainty Estimation with Infinitesimal Jackknife, Its Distribution and MeanField Approximation
Uncertainty quantification is an important research area in machine lear...
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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...
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Learning to Walk via Deep Reinforcement Learning
Deep reinforcement learning suggests the promise of fully automated lear...
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On the Convergence of Adam and Beyond
Several recently proposed stochastic optimization methods that have been...
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3D Conditional Generative Adversarial Networks to enable largescale seismic image enhancement
We propose GANbased image enhancement models for frequency enhancement ...
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Adaptive Scheduling for MultiTask Learning
To train neural machine translation models simultaneously on multiple ta...
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CAQL: Continuous Action QLearning
Valuebased reinforcement learning (RL) methods like Qlearning have sho...
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Generative Models for Effective ML on Private, Decentralized Datasets
To improve realworld applications of machine learning, experienced mode...
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NoRML: NoReward Meta Learning
Efficiently adapting to new environments and changes in dynamics is crit...
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Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE...
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A Deep Factorization of Style and Structure in Fonts
We propose a deep factorization model for typographic analysis that dise...
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Behavior Regularized Offline Reinforcement Learning
In reinforcement learning (RL) research, it is common to assume access t...
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An Analysis of Object Representations in Deep Visual Trackers
Fully convolutional deep correlation networks are integral components of...
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