Stanford University

research

∙ 03/30/2022

Convergence of gradient descent for deep neural networks

Optimization by gradient descent has been one of main drivers of the "de...

Sourav Chatterjee, et al. ∙

research

∙ 12/09/2019

Efficient Object Detection in Large Images using Deep Reinforcement Learning

Traditionally, an object detector is applied to every part of the scene ...

Burak Uzkent, et al. ∙

research

∙ 01/31/2020

Stable Prediction with Model Misspecification and Agnostic Distribution Shift

For many machine learning algorithms, two main assumptions are required ...

Kun Kuang, et al. ∙

research

∙ 12/03/2019

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

Deep learning frameworks have often focused on either usability or speed...

Adam Paszke, et al. ∙

research

∙ 09/28/2019

Regression Planning Networks

Recent learning-to-plan methods have shown promising results on planning...

Danfei Xu, et al. ∙

research

∙ 12/19/2019

An Application of Generative Adversarial Networks for Super Resolution Medical Imaging

Acquiring High Resolution (HR) Magnetic Resonance (MR) images requires t...

Rewa Sood, 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

∙ 03/12/2021

Large Batch Simulation for Deep Reinforcement Learning

We accelerate deep reinforcement learning-based training in visually com...

Brennan Shacklett, et al. ∙

research

∙ 06/05/2020

Evaluating the Disentanglement of Deep Generative Models through Manifold Topology

Learning disentangled representations is regarded as a fundamental task ...

Sharon Zhou, et al. ∙

research

∙ 12/29/2019

Computational model discovery with reinforcement learning

The motivation of this study is to leverage recent breakthroughs in arti...

Maxime Bassenne, et al. ∙

research

∙ 12/02/2019

Deep Bayesian Active Learning for Multiple Correct Outputs

Typical active learning strategies are designed for tasks, such as class...

Khaled Jedoui, et al. ∙

research

∙ 07/30/2018

Local Linear Forests

Random forests are a powerful method for non-parametric regression, but ...

Rina Friedberg, et al. ∙

research

∙ 04/18/2019

4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks

In many robotics and VR/AR applications, 3D-videos are readily-available...

Christopher Choy, et al. ∙

research

∙ 07/09/2019

Learning by Abstraction: The Neural State Machine

We introduce the Neural State Machine, seeking to bridge the gap between...

Drew A. Hudson, et al. ∙

research

∙ 06/24/2020

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...

Erdem Bıyık, et al. ∙

research

∙ 04/21/2019

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

Unsupervised image-to-image translation aims at learning a mapping betwe...

Wayne Wu, et al. ∙

research

∙ 12/06/2019

cFineGAN: Unsupervised multi-conditional fine-grained image generation

We propose an unsupervised multi-conditional image generation pipeline: ...

Gunjan Aggarwal, et al. ∙

research

∙ 10/28/2021

From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence

Machine learning has long since become a keystone technology, accelerati...

Nicholas Roy, et al. ∙

research

∙ 12/20/2019

Adversarial Representation Active Learning

Active learning aims to develop label-efficient algorithms by querying t...

Ali Mottaghi, et al. ∙

research

∙ 05/20/2022

Can Foundation Models Wrangle Your Data?

Foundation Models (FMs) are models trained on large corpora of data that...

Avanika Narayan, et al. ∙

research

∙ 01/05/2022

Deep Learning-Based Sparse Whole-Slide Image Analysis for the Diagnosis of Gastric Intestinal Metaplasia

In recent years, deep learning has successfully been applied to automate...

Jon Braatz, et al. ∙

research

∙ 01/27/2020

Predicting Regression Probability Distributions with Imperfect Data Through Optimal Transformations

The goal of regression analysis is to predict the value of a numeric out...

Jerome H. Friedman, et al. ∙

research

∙ 03/11/2020

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...

Yiping Lu, et al. ∙

research

∙ 06/12/2020

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

Reinforcement learning algorithms can acquire policies for complex tasks...

Russell Mendonca, 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

∙ 07/29/2021

Contemporary Symbolic Regression Methods and their Relative Performance

Many promising approaches to symbolic regression have been presented in ...

William La Cava, et al. ∙

research

∙ 10/23/2021

Why Machine Learning Cannot Ignore Maximum Likelihood Estimation

The growth of machine learning as a field has been accelerating with inc...

Mark J. van der Laan, et al. ∙

research

∙ 01/14/2020

ImagineNet: Restyling Apps Using Neural Style Transfer

This paper presents ImagineNet, a tool that uses a novel neural style tr...

Michael H. Fischer, et al. ∙

research

∙ 10/10/2019

Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards

Hierarchical Reinforcement Learning (HRL) is a promising approach to sol...

Siyuan Li, et al. ∙

research

∙ 12/15/2019

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

Action recognition has typically treated actions and activities as monol...

Jingwei Ji, et al. ∙

research

∙ 12/08/2019

Neural Network Generalization: The impact of camera parameters

We quantify the generalization of a convolutional neural network (CNN) t...

Zhenyi Liu, et al. ∙

research

∙ 11/01/2021

Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems

Recurrent neural networks (RNNs) are powerful models for processing time...

Jimmy T. H. Smith, et al. ∙

research

∙ 04/20/2020

Roundtrip: A Deep Generative Neural Density Estimator

Density estimation is a fundamental problem in both statistics and machi...

Qiao Liu, et al. ∙

research

∙ 06/12/2019

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

Scene graph prediction --- classifying the set of objects and predicates...

Apoorva Dornadula, et al. ∙

research

∙ 06/24/2020

Compositional Explanations of Neurons

We describe a procedure for explaining neurons in deep representations b...

Jesse Mu, et al. ∙

research

∙ 06/14/2020

Generative 3D Part Assembly via Dynamic Graph Learning

Autonomous part assembly is a challenging yet crucial task in 3D compute...

Jialei Huang, et al. ∙

research

∙ 11/08/2019

Convex Hierarchical Clustering for Graph-Structured Data

Convex clustering is a recent stable alternative to hierarchical cluster...

Claire Donnat, et al. ∙

research

∙ 11/21/2019

Automatically Neutralizing Subjective Bias in Text

Texts like news, encyclopedias, and some social media strive for objecti...

Reid Pryzant, et al. ∙

research

∙ 02/25/2020

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...

Elaina Chai, et al. ∙

research

∙ 08/26/2019

Sufficient Representations for Categorical Variables

Many learning algorithms require categorical data to be transformed into...

Jonathan Johannemann, et al. ∙

research

∙ 05/29/2019

Pre-training Graph Neural Networks

Many applications of machine learning in science and medicine, including...

Weihua Hu, et al. ∙

research

∙ 10/10/2019

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

Robots can learn the right reward function by querying a human expert. E...

Erdem Bıyık, et al. ∙

research

∙ 01/03/2020

Question Type Classification Methods Comparison

The paper presents a comparative study of state-of-the-art approaches fo...

Tamirlan Seidakhmetov, et al. ∙

research

∙ 11/17/2020

Design Space for Graph Neural Networks

The rapid evolution of Graph Neural Networks (GNNs) has led to a growing...

Jiaxuan You, et al. ∙

research

∙ 12/06/2018

Online Model Distillation for Efficient Video Inference

High-quality computer vision models typically address the problem of und...

Ravi Teja Mullapudi, et al. ∙

research

∙ 03/11/2019

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

Reinforcement learning (RL) is a promising data-driven approach for adap...

Tianshu Chu, et al. ∙

research

∙ 03/12/2020

AirSim Drone Racing Lab

Autonomous drone racing is a challenging research problem at the interse...

Ratnesh Madaan, et al. ∙

research

∙ 09/08/2019

Imitation Learning for Human Pose Prediction

Modeling and prediction of human motion dynamics has long been a challen...

Borui Wang, et al. ∙

research

∙ 07/06/2021

NRST: Non-rigid Surface Tracking from Monocular Video

We propose an efficient method for non-rigid surface tracking from monoc...

Marc Habermann, et al. ∙

research

∙ 11/19/2021

YMIR: A Rapid Data-centric Development Platform for Vision Applications

This paper introduces an open source platform for rapid development of c...

Phoenix X. Huang, et al. ∙

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