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Bayesian Optimization in AlphaGo
During the development of AlphaGo, its many hyper-parameters were tuned ...
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Machine learning accelerated computational fluid dynamics
Numerical simulation of fluids plays an essential role in modeling many ...
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Self-Supervised MultiModal Versatile Networks
Videos are a rich source of multi-modal supervision. In this work, we le...
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RealFormer: Transformer Likes Residual Attention
Transformer is the backbone of modern NLP models. In this paper, we prop...
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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 Energy-based Model with Flow-based Backbone by Neural Transport MCMC
Learning energy-based model (EBM) requires MCMC sampling of the learned ...
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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...
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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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How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned
Deep reinforcement learning (RL) has emerged as a promising approach for...
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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
Transformer-based models have pushed the state of the art in many natura...
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Towards Causal Representation Learning
The two fields of machine learning and graphical causality arose and dev...
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Flow Contrastive Estimation of Energy-Based Models
This paper studies a training method to jointly estimate an energy-based...
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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...
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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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Low-Weight 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 Detail-Invariance and the Abstractions Learned by Deep Networks
Deep networks have achieved excellent results in perceptual tasks, yet t...
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Global Self-Attention Networks for Image Recognition
Recently, a series of works in computer vision have shown promising resu...
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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 ...
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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 sparse-reward, continuous con...
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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,...
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In-domain 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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End-to-End 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 Human-Level Language Understanding
Language is central to human intelligence. We review recent breakthrough...
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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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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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The Garden of Forking Paths: Towards Multi-Future 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 Mean-Field 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 large-scale seismic image enhancement
We propose GAN-based image enhancement models for frequency enhancement ...
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Adaptive Scheduling for Multi-Task Learning
To train neural machine translation models simultaneously on multiple ta...
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CAQL: Continuous Action Q-Learning
Value-based reinforcement learning (RL) methods like Q-learning have sho...
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Generative Models for Effective ML on Private, Decentralized Datasets
To improve real-world applications of machine learning, experienced mode...
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