cornell university

∙ Ithaca , United States ∙ 0 followers

Cornell University is an educational institution that offers undergraduate majors, graduate, undergraduate and advanced degrees, and continuing education and outreach programs. Cornell has seven undergraduate colleges; a graduate school; professional-degree schools in business management, medicine, law, and veterinary medicine; and affiliated faculty units. Cornell University was founded in 1865 and is based in Ithaca, New York.

Torch-Struct: Deep Structured Prediction Library

The literature on structured prediction for NLP describes a rich collect...
02/03/2020 ∙ by Alexander M. Rush, et al. ∙ 48 ∙ share

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

Recent studies have discovered the vulnerability of Deep Neural Networks...
04/16/2019 ∙ by Xiaosen Wang, et al. ∙ 38 ∙ share

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AutoML using Metadata Language Embeddings

As a human choosing a supervised learning algorithm, it is natural to be...
10/08/2019 ∙ by Iddo Drori, et al. ∙ 36 ∙ share

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Rational neural networks

We consider neural networks with rational activation functions. The choi...
04/04/2020 ∙ by Nicolas Boullé, et al. ∙ 33 ∙ share

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Cascaded Text Generation with Markov Transformers

The two dominant approaches to neural text generation are fully autoregr...
06/01/2020 ∙ by Yuntian Deng, et al. ∙ 31 ∙ share

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Mixed Reinforcement Learning with Additive Stochastic Uncertainty

Reinforcement learning (RL) methods often rely on massive exploration da...
02/28/2020 ∙ by Yao Mu, et al. ∙ 30 ∙ share

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Why is Attention Not So Attentive?

Attention-based methods have played an important role in model interpret...
06/10/2020 ∙ by Bing Bai, et al. ∙ 30 ∙ share

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Blind Backdoors in Deep Learning Models

We investigate a new method for injecting backdoors into machine learnin...
05/08/2020 ∙ by Eugene Bagdasaryan, et al. ∙ 26 ∙ share

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Neural Manifold Ordinary Differential Equations

To better conform to data geometry, recent deep generative modelling tec...
06/18/2020 ∙ by Aaron Lou, et al. ∙ 25 ∙ share

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Convolutional Networks with Dense Connectivity

Recent work has shown that convolutional networks can be substantially d...
01/08/2020 ∙ by Gao Huang, et al. ∙ 24 ∙ share

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A Simple Baseline for Bayesian Uncertainty in Deep Learning

We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose ...
02/07/2019 ∙ by Wesley Maddox, et al. ∙ 20 ∙ share

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

Detecting objects such as cars and pedestrians in 3D plays an indispensa...
06/14/2019 ∙ by Yurong You, et al. ∙ 20 ∙ share

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When Does Self-supervision Improve Few-shot Learning?

We present a technique to improve the generalization of deep representat...
10/08/2019 ∙ by Jong-Chyi Su, et al. ∙ 19 ∙ share

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Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning

In real world applications like healthcare, it is usually difficult to b...
07/24/2020 ∙ by Yiqin Yu, et al. ∙ 19 ∙ share

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Blameworthiness in Multi-Agent Settings

We provide a formal definition of blameworthiness in settings where mult...
03/11/2019 ∙ by Meir Friedenberg, et al. ∙ 18 ∙ share

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

In the domain of autonomous driving, deep learning has substantially imp...
05/17/2020 ∙ by Yan Wang, et al. ∙ 18 ∙ share

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Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation

Federated learning is a setting where agents, each with access to their ...
10/02/2020 ∙ by Kate Donahue, et al. ∙ 17 ∙ share

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CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning

The ability to perform offline A/B-testing and off-policy learning using...
11/06/2018 ∙ by Yi Su, et al. ∙ 16 ∙ share

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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...
12/18/2018 ∙ by Yan Wang, et al. ∙ 16 ∙ share

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Confidence Calibration for Convolutional Neural Networks Using Structured Dropout

In classification applications, we often want probabilistic predictions ...
06/23/2019 ∙ by Zhilu Zhang, et al. ∙ 16 ∙ share

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Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

In this paper, we propose a fast surrogate leverage weighted sampling st...
11/20/2019 ∙ by Fanghui Liu, et al. ∙ 16 ∙ share

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

In this work, we demonstrate that receptive fields in 3D pose estimation...
05/28/2020 ∙ by Michael Shin, et al. ∙ 15 ∙ share

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Detecting and Tracking Communal Bird Roosts in Weather Radar Data

The US weather radar archive holds detailed information about biological...
04/24/2020 ∙ by Zezhou Cheng, et al. ∙ 14 ∙ share

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Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces

Classical deformable registration techniques achieve impressive results ...
03/08/2019 ∙ by Adrian V. Dalca, et al. ∙ 13 ∙ share

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Efficient Rollout Strategies for Bayesian Optimization

Bayesian optimization (BO) is a class of sample-efficient global optimiz...
02/24/2020 ∙ by Eric Hans Lee, et al. ∙ 13 ∙ share

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Unsupervised Data Imputation via Variational Inference of Deep Subspaces

A wide range of systems exhibit high dimensional incomplete data. Accura...
03/08/2019 ∙ by Adrian V. Dalca, et al. ∙ 11 ∙ share

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Robust Local Features for Improving the Generalization of Adversarial Training

Adversarial training has been demonstrated as one of the most effective ...
09/23/2019 ∙ by Chubiao Song, et al. ∙ 11 ∙ share

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Efficient Policy Learning from Surrogate-Loss Classification Reductions

Recent work on policy learning from observational data has highlighted t...
02/12/2020 ∙ by Andrew Bennett, et al. ∙ 11 ∙ share

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

A contextual bandit problem is studied in a highly non-stationary enviro...
02/29/2020 ∙ by Xiao Xu, et al. ∙ 11 ∙ share

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Low-volatility Anomaly and the Adaptive Multi-Factor Model

The paper explains the low-volatility anomaly from a new perspective. We...
03/16/2020 ∙ by Robert A. Jarrow, et al. ∙ 11 ∙ share

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Low Frequency Adversarial Perturbation

Recently, machine learning security has received significant attention. ...
09/24/2018 ∙ by Chuan Guo, et al. ∙ 10 ∙ share

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Neural Naturalist: Generating Fine-Grained Image Comparisons

We introduce the new Birds-to-Words dataset of 41k sentences describing ...
09/09/2019 ∙ by Maxwell Forbes, et al. ∙ 10 ∙ share

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

As artificial intelligence becomes increasingly intelligent—in some case...
06/02/2020 ∙ by Reid McIlroy-Young, et al. ∙ 10 ∙ share

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Decomposition of Longitudinal Deformations via Beltrami Descriptors

We present a mathematical model to decompose a longitudinal deformation ...
08/06/2020 ∙ by Ho Law, et al. ∙ 10 ∙ share

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Retouchdown: Adding Touchdown to StreetLearn as a Shareable Resource for Language Grounding Tasks in Street View

The Touchdown dataset (Chen et al., 2019) provides instructions by human...
01/10/2020 ∙ by Harsh Mehta, et al. ∙ 9 ∙ share

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MOReL : Model-Based Offline Reinforcement Learning

In offline reinforcement learning (RL), the goal is to learn a successfu...
05/12/2020 ∙ by Rahul Kidambi, et al. ∙ 9 ∙ share

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Smoothed Analysis of Online and Differentially Private Learning

Practical and pervasive needs for robustness and privacy in algorithms h...
06/17/2020 ∙ by Nika Haghtalab, et al. ∙ 9 ∙ share

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Autonomous synthesis of metastable materials

Autonomous experimentation enabled by artificial intelligence (AI) offer...
01/19/2021 ∙ by Sebastian Ament, et al. ∙ 9 ∙ share

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Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction

Identifying changes in model parameters is fundamental in machine learni...
10/28/2018 ∙ by William Herlands, et al. ∙ 8 ∙ share

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Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition

Recurrent Neural Networks (RNNs) and their variants, such as Long-Short ...
11/19/2018 ∙ by Yu Pan, et al. ∙ 8 ∙ share

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Disentangling Influence: Using Disentangled Representations to Audit Model Predictions

Motivated by the need to audit complex and black box models, there has b...
06/20/2019 ∙ by Charles T. Marx, et al. ∙ 8 ∙ share

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Fair Learning-to-Rank from Implicit Feedback

Addressing unfairness in rankings has become an increasingly important p...
11/19/2019 ∙ by Himank Yadav, et al. ∙ 8 ∙ share

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On the Distribution of Minima in Intrinsic-Metric Rotation Averaging

Rotation Averaging is a non-convex optimization problem that determines ...
03/18/2020 ∙ by Kyle Wilson, et al. ∙ 8 ∙ share

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Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition

Few-shot, fine-grained classification requires a model to learn subtle, ...
04/01/2020 ∙ by Luming Tang, et al. ∙ 8 ∙ share

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An Auto-Encoder Strategy for Adaptive Image Segmentation

Deep neural networks are powerful tools for biomedical image segmentatio...
04/29/2020 ∙ by Evan M. Yu, et al. ∙ 8 ∙ share

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GeoStyle: Discovering Fashion Trends and Events

Understanding fashion styles and trends is of great potential interest t...
08/29/2019 ∙ by Utkarsh Mall, et al. ∙ 7 ∙ share

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Boosting Supervision with Self-Supervision for Few-shot Learning

We present a technique to improve the transferability of deep representa...
06/17/2019 ∙ by Jong-Chyi Su, et al. ∙ 6 ∙ share

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End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

Reliable and accurate 3D object detection is a necessity for safe autono...
04/07/2020 ∙ by Rui Qian, et al. ∙ 6 ∙ share

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Stable Reinforcement Learning with Unbounded State Space

We consider the problem of reinforcement learning (RL) with unbounded st...
06/08/2020 ∙ by Devavrat Shah, et al. ∙ 6 ∙ share

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A Markov Decision Process Approach to Active Meta Learning

In supervised learning, we fit a single statistical model to a given dat...
09/10/2020 ∙ by Bingjia Wang, et al. ∙ 6 ∙ share

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