cornell university

research

∙ 04/27/2021

Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems

Deep neural networks have become a pervasive tool in science and enginee...

Logan G. Wright, et al. ∙

research

∙ 12/02/2021

Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification

Multi-label classification (MLC) is a prediction task where each sample ...

Junwen Bai, et al. ∙

research

∙ 02/03/2020

Torch-Struct: Deep Structured Prediction Library

The literature on structured prediction for NLP describes a rich collect...

Alexander M. Rush, et al. ∙

research

∙ 04/16/2019

AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets

Recent studies have discovered the vulnerability of Deep Neural Networks...

Xiaosen Wang, et al. ∙

research

∙ 10/08/2019

AutoML using Metadata Language Embeddings

As a human choosing a supervised learning algorithm, it is natural to be...

Iddo Drori, et al. ∙

research

∙ 04/04/2020

Rational neural networks

We consider neural networks with rational activation functions. The choi...

Nicolas Boullé, et al. ∙

research

∙ 03/29/2022

Learning to Detect Mobile Objects from LiDAR Scans Without Labels

Current 3D object detectors for autonomous driving are almost entirely t...

Yurong You, et al. ∙

research

∙ 06/01/2020

Cascaded Text Generation with Markov Transformers

The two dominant approaches to neural text generation are fully autoregr...

Yuntian Deng, et al. ∙

research

∙ 02/28/2020

Mixed Reinforcement Learning with Additive Stochastic Uncertainty

Reinforcement learning (RL) methods often rely on massive exploration da...

Yao Mu, et al. ∙

research

∙ 06/10/2020

Why is Attention Not So Attentive?

Attention-based methods have played an important role in model interpret...

Bing Bai, et al. ∙

research

∙ 11/17/2021

A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction

Climate change is posing new challenges to crop-related concerns includi...

Joshua Fan, et al. ∙

research

∙ 05/08/2020

Blind Backdoors in Deep Learning Models

We investigate a new method for injecting backdoors into machine learnin...

Eugene Bagdasaryan, et al. ∙

research

∙ 06/18/2020

Neural Manifold Ordinary Differential Equations

To better conform to data geometry, recent deep generative modelling tec...

Aaron Lou, et al. ∙

research

∙ 01/08/2020

Convolutional Networks with Dense Connectivity

Recent work has shown that convolutional networks can be substantially d...

Gao Huang, et al. ∙

research

∙ 04/22/2021

VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

We present a framework for learning multimodal representations from unla...

Hassan Akbari, et al. ∙

research

∙ 02/07/2019

A Simple Baseline for Bayesian Uncertainty in Deep Learning

We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose ...

Wesley Maddox, et al. ∙

research

∙ 06/14/2019

Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving

Detecting objects such as cars and pedestrians in 3D plays an indispensa...

Yurong You, et al. ∙

research

∙ 10/08/2019

When Does Self-supervision Improve Few-shot Learning?

We present a technique to improve the generalization of deep representat...

Jong-Chyi Su, et al. ∙

research

∙ 07/24/2020

Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning

In real world applications like healthcare, it is usually difficult to b...

Yiqin Yu, et al. ∙

research

∙ 07/19/2021

Equivariant Manifold Flows

Tractably modelling distributions over manifolds has long been an import...

Isay Katsman, et al. ∙

research

∙ 05/30/2022

Gradient Backpropagation Through Combinatorial Algorithms: Identity with Projection Works

Embedding discrete solvers as differentiable layers has given modern dee...

Subham Sekhar Sahoo, et al. ∙

research

∙ 03/11/2019

Blameworthiness in Multi-Agent Settings

We provide a formal definition of blameworthiness in settings where mult...

Meir Friedenberg, et al. ∙

research

∙ 05/17/2020

Train in Germany, Test in The USA: Making 3D Object Detectors Generalize

In the domain of autonomous driving, deep learning has substantially imp...

Yan Wang, et al. ∙

research

∙ 10/02/2020

Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation

Federated learning is a setting where agents, each with access to their ...

Kate Donahue, et al. ∙

research

∙ 01/08/2022

Low-Rank Constraints for Fast Inference in Structured Models

Structured distributions, i.e. distributions over combinatorial spaces, ...

Justin T. Chiu, et al. ∙

research

∙ 11/06/2018

CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning

The ability to perform offline A/B-testing and off-policy learning using...

Yi Su, et al. ∙

research

∙ 12/18/2018

Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

3D object detection is an essential task in autonomous driving. Recent t...

Yan Wang, et al. ∙

research

∙ 06/23/2019

Confidence Calibration for Convolutional Neural Networks Using Structured Dropout

In classification applications, we often want probabilistic predictions ...

Adrian V. Dalca, et al. ∙

research

∙ 11/20/2019

Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

In this paper, we propose a fast surrogate leverage weighted sampling st...

Fanghui Liu, et al. ∙

research

∙ 10/22/2021

Contrastively Disentangled Sequential Variational Autoencoder

Self-supervised disentangled representation learning is a critical task ...

Junwen Bai, et al. ∙

research

∙ 05/28/2020

3D human pose estimation with adaptive receptive fields and dilated temporal convolutions

In this work, we demonstrate that receptive fields in 3D pose estimation...

Michael Shin, et al. ∙

research

∙ 04/24/2020

Detecting and Tracking Communal Bird Roosts in Weather Radar Data

The US weather radar archive holds detailed information about biological...

Zezhou Cheng, et al. ∙

research

∙ 09/14/2021

Rationales for Sequential Predictions

Sequence models are a critical component of modern NLP systems, but thei...

Keyon Vafa, et al. ∙

research

∙ 10/27/2021

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Many widely used datasets for graph machine learning tasks have generall...

Derek Lim, et al. ∙

research

∙ 03/08/2019

Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces

Classical deformable registration techniques achieve impressive results ...

Adrian V. Dalca, et al. ∙

research

∙ 02/24/2020

Efficient Rollout Strategies for Bayesian Optimization

Bayesian optimization (BO) is a class of sample-efficient global optimiz...

Eric Hans Lee, et al. ∙

research

∙ 09/29/2021

Equivariant Neural Network for Factor Graphs

Several indices used in a factor graph data structure can be permuted wi...

Fan-Yun Sun, et al. ∙

research

∙ 10/08/2021

Quantifying Inequality in Underreported Medical Conditions

Estimating the prevalence of a medical condition, or the proportion of t...

Divya Shanmugam, et al. ∙

research

∙ 06/17/2022

A Flexible Diffusion Model

Diffusion (score-based) generative models have been widely used for mode...

Weitao Du, et al. ∙

research

∙ 03/08/2019

Unsupervised Data Imputation via Variational Inference of Deep Subspaces

A wide range of systems exhibit high dimensional incomplete data. Accura...

Adrian V. Dalca, et al. ∙

research

∙ 09/23/2019

Robust Local Features for Improving the Generalization of Adversarial Training

Adversarial training has been demonstrated as one of the most effective ...

Chubiao Song, et al. ∙

research

∙ 02/12/2020

Efficient Policy Learning from Surrogate-Loss Classification Reductions

Recent work on policy learning from observational data has highlighted t...

Andrew Bennett, et al. ∙

research

∙ 02/29/2020

Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests

A contextual bandit problem is studied in a highly non-stationary enviro...

Xiao Xu, et al. ∙

research

∙ 03/16/2020

Low-volatility Anomaly and the Adaptive Multi-Factor Model

The paper explains the low-volatility anomaly from a new perspective. We...

Robert A. Jarrow, et al. ∙

research

∙ 07/06/2021

Integrating Circle Kernels into Convolutional Neural Networks

The square kernel is a standard unit for contemporary Convolutional Neur...

Kun He, et al. ∙

research

∙ 09/24/2018

Low Frequency Adversarial Perturbation

Recently, machine learning security has received significant attention. ...

Chuan Guo, et al. ∙

research

∙ 09/09/2019

Neural Naturalist: Generating Fine-Grained Image Comparisons

We introduce the new Birds-to-Words dataset of 41k sentences describing ...

Maxwell Forbes, et al. ∙

research

∙ 06/02/2020

Aligning Superhuman AI and Human Behavior: Chess as a Model System

As artificial intelligence becomes increasingly intelligent—in some case...

Reid McIlroy-Young, et al. ∙

research

∙ 08/06/2020

Decomposition of Longitudinal Deformations via Beltrami Descriptors

We present a mathematical model to decompose a longitudinal deformation ...

Ho Law, et al. ∙

research

∙ 09/09/2021

Extreme Bandits using Robust Statistics

We consider a multi-armed bandit problem motivated by situations where o...

Sujay Bhatt, et al. ∙

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