Stanford University

∙ Stanford , United States ∙ 0 followers

Stanford University, one of the world's leading teaching and research universities, is dedicated to finding solutions to big challenges and to preparing students for leadership in a complex world.

Featured Researchers

Sergey Levine
256 publications
Yu Zhang
224 publications
Rui Wang
176 publications
Hao Wang
175 publications
Jian Zhang
136 publications
Li Zhang
134 publications
Stefano Ermon
115 publications
Li Fei-Fei
109 publications
Percy Liang
107 publications
Silvio Savarese
103 publications
Jure Leskovec
100 publications

Efficient Object Detection in Large Images using Deep Reinforcement Learning

Traditionally, an object detector is applied to every part of the scene ...
12/09/2019 ∙ by Burak Uzkent, et al. ∙ 98 ∙ share

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Stable Prediction with Model Misspecification and Agnostic Distribution Shift

For many machine learning algorithms, two main assumptions are required ...
01/31/2020 ∙ by Kun Kuang, et al. ∙ 95 ∙ share

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PyTorch: An Imperative Style, High-Performance Deep Learning Library

Deep learning frameworks have often focused on either usability or speed...
12/03/2019 ∙ by Adam Paszke, et al. ∙ 90 ∙ share

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Regression Planning Networks

Recent learning-to-plan methods have shown promising results on planning...
09/28/2019 ∙ by Danfei Xu, et al. ∙ 87 ∙ share

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An Application of Generative Adversarial Networks for Super Resolution Medical Imaging

Acquiring High Resolution (HR) Magnetic Resonance (MR) images requires t...
12/19/2019 ∙ by Rewa Sood, et al. ∙ 68 ∙ 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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Large Batch Simulation for Deep Reinforcement Learning

We accelerate deep reinforcement learning-based training in visually com...
03/12/2021 ∙ by Brennan Shacklett, et al. ∙ 61 ∙ share

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Evaluating the Disentanglement of Deep Generative Models through Manifold Topology

Learning disentangled representations is regarded as a fundamental task ...
06/05/2020 ∙ by Sharon Zhou, et al. ∙ 59 ∙ share

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Computational model discovery with reinforcement learning

The motivation of this study is to leverage recent breakthroughs in arti...
12/29/2019 ∙ by Maxime Bassenne, et al. ∙ 58 ∙ share

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Deep Bayesian Active Learning for Multiple Correct Outputs

Typical active learning strategies are designed for tasks, such as class...
12/02/2019 ∙ by Khaled Jedoui, et al. ∙ 53 ∙ share

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Local Linear Forests

Random forests are a powerful method for non-parametric regression, but ...
07/30/2018 ∙ by Rina Friedberg, et al. ∙ 52 ∙ share

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4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks

In many robotics and VR/AR applications, 3D-videos are readily-available...
04/18/2019 ∙ by Christopher Choy, et al. ∙ 50 ∙ share

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Learning by Abstraction: The Neural State Machine

We introduce the Neural State Machine, seeking to bridge the gap between...
07/09/2019 ∙ by Drew A. Hudson, et al. ∙ 49 ∙ share

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Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferences

Reward functions are a common way to specify the objective of a robot. A...
06/24/2020 ∙ by Erdem Bıyık, et al. ∙ 49 ∙ share

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TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation

Unsupervised image-to-image translation aims at learning a mapping betwe...
04/21/2019 ∙ by Wayne Wu, et al. ∙ 48 ∙ share

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cFineGAN: Unsupervised multi-conditional fine-grained image generation

We propose an unsupervised multi-conditional image generation pipeline: ...
12/06/2019 ∙ by Gunjan Aggarwal, et al. ∙ 48 ∙ share

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Adversarial Representation Active Learning

Active learning aims to develop label-efficient algorithms by querying t...
12/20/2019 ∙ by Ali Mottaghi, et al. ∙ 44 ∙ share

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Predicting Regression Probability Distributions with Imperfect Data Through Optimal Transformations

The goal of regression analysis is to predict the value of a numeric out...
01/27/2020 ∙ by Jerome H. Friedman, et al. ∙ 42 ∙ share

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A Mean-field Analysis of Deep ResNet and Beyond: Towards Provable Optimization Via Overparameterization From Depth

Training deep neural networks with stochastic gradient descent (SGD) can...
03/11/2020 ∙ by Yiping Lu, et al. ∙ 42 ∙ share

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Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling

Reinforcement learning algorithms can acquire policies for complex tasks...
06/12/2020 ∙ by Russell Mendonca, 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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ImagineNet: Restyling Apps Using Neural Style Transfer

This paper presents ImagineNet, a tool that uses a novel neural style tr...
01/14/2020 ∙ by Michael H. Fischer, et al. ∙ 38 ∙ share

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Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards

Hierarchical Reinforcement Learning (HRL) is a promising approach to sol...
10/10/2019 ∙ by Siyuan Li, et al. ∙ 37 ∙ share

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Action Genome: Actions as Composition of Spatio-temporal Scene Graphs

Action recognition has typically treated actions and activities as monol...
12/15/2019 ∙ by Jingwei Ji, et al. ∙ 35 ∙ share

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Neural Network Generalization: The impact of camera parameters

We quantify the generalization of a convolutional neural network (CNN) t...
12/08/2019 ∙ by Zhenyi Liu, et al. ∙ 33 ∙ share

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Roundtrip: A Deep Generative Neural Density Estimator

Density estimation is a fundamental problem in both statistics and machi...
04/20/2020 ∙ by Qiao Liu, et al. ∙ 32 ∙ share

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Visual Relationships as Functions: Enabling Few-Shot Scene Graph Prediction

Scene graph prediction --- classifying the set of objects and predicates...
06/12/2019 ∙ by Apoorva Dornadula, et al. ∙ 29 ∙ share

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Compositional Explanations of Neurons

We describe a procedure for explaining neurons in deep representations b...
06/24/2020 ∙ by Jesse Mu, et al. ∙ 28 ∙ share

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Generative 3D Part Assembly via Dynamic Graph Learning

Autonomous part assembly is a challenging yet crucial task in 3D compute...
06/14/2020 ∙ by Jialei Huang, et al. ∙ 27 ∙ share

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Convex Hierarchical Clustering for Graph-Structured Data

Convex clustering is a recent stable alternative to hierarchical cluster...
11/08/2019 ∙ by Claire Donnat, et al. ∙ 26 ∙ share

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Automatically Neutralizing Subjective Bias in Text

Texts like news, encyclopedias, and some social media strive for objecti...
11/21/2019 ∙ by Reid Pryzant, et al. ∙ 26 ∙ share

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Separating the Effects of Batch Normalization on CNN Training Speed and Stability Using Classical Adaptive Filter Theory

Batch Normalization (BatchNorm) is commonly used in Convolutional Neural...
02/25/2020 ∙ by Elaina Chai, et al. ∙ 26 ∙ share

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Sufficient Representations for Categorical Variables

Many learning algorithms require categorical data to be transformed into...
08/26/2019 ∙ by Jonathan Johannemann, et al. ∙ 25 ∙ share

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Pre-training Graph Neural Networks

Many applications of machine learning in science and medicine, including...
05/29/2019 ∙ by Weihua Hu, et al. ∙ 24 ∙ share

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Asking Easy Questions: A User-Friendly Approach to Active Reward Learning

Robots can learn the right reward function by querying a human expert. E...
10/10/2019 ∙ by Erdem Bıyık, et al. ∙ 24 ∙ share

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Question Type Classification Methods Comparison

The paper presents a comparative study of state-of-the-art approaches fo...
01/03/2020 ∙ by Tamirlan Seidakhmetov, et al. ∙ 24 ∙ share

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Design Space for Graph Neural Networks

The rapid evolution of Graph Neural Networks (GNNs) has led to a growing...
11/17/2020 ∙ by Jiaxuan You, et al. ∙ 24 ∙ share

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Online Model Distillation for Efficient Video Inference

High-quality computer vision models typically address the problem of und...
12/06/2018 ∙ by Ravi Teja Mullapudi, et al. ∙ 22 ∙ share

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Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control

Reinforcement learning (RL) is a promising data-driven approach for adap...
03/11/2019 ∙ by Tianshu Chu, et al. ∙ 22 ∙ share

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AirSim Drone Racing Lab

Autonomous drone racing is a challenging research problem at the interse...
03/12/2020 ∙ by Ratnesh Madaan, et al. ∙ 22 ∙ share

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Imitation Learning for Human Pose Prediction

Modeling and prediction of human motion dynamics has long been a challen...
09/08/2019 ∙ by Borui Wang, et al. ∙ 21 ∙ share

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Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation

Existing Rademacher complexity bounds for neural networks rely only on n...
05/09/2019 ∙ by Colin Wei, et al. ∙ 20 ∙ share

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Deep Reinforcement Learning for Constrained Field Development Optimization in Subsurface Two-phase Flow

We present a deep reinforcement learning-based artificial intelligence a...
03/31/2021 ∙ by Yusuf Nasir, et al. ∙ 20 ∙ share

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Accelerating cardiac cine MRI beyond compressed sensing using DL-ESPIRiT

A novel neural network architecture, known as DL-ESPIRiT, is proposed to...
11/13/2019 ∙ by Christopher M. Sandino, et al. ∙ 19 ∙ share

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Meta-Learning without Memorization

The ability to learn new concepts with small amounts of data is a critic...
12/09/2019 ∙ by Mingzhang Yin, et al. ∙ 19 ∙ share

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Deep Optics for Monocular Depth Estimation and 3D Object Detection

Depth estimation and 3D object detection are critical for scene understa...
04/18/2019 ∙ by Julie Chang, et al. ∙ 18 ∙ share

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Playing Go without Game Tree Search Using Convolutional Neural Networks

The game of Go has a long history in East Asian countries, but the field...
07/02/2019 ∙ by Jeffrey Barratt, et al. ∙ 18 ∙ share

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Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects

We demonstrate model-based, visual robot manipulation of linear deformab...
11/14/2019 ∙ by Mengyuan Yan, et al. ∙ 18 ∙ share

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Predicting the Physical Dynamics of Unseen 3D Objects

Machines that can predict the effect of physical interactions on the dyn...
01/16/2020 ∙ by Davis Rempe, et al. ∙ 18 ∙ share

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A Delay Metric for Video Object Detection: What Average Precision Fails to Tell

Average precision (AP) is a widely used metric to evaluate detection acc...
08/18/2019 ∙ by Huizi Mao, et al. ∙ 17 ∙ share

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