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Featured Researchers

Lawrence Carin
159 publications
Dong Wang
120 publications
Yan Zhang
120 publications
Yu Wang
113 publications
Jun Zhang
106 publications
Ming Li
102 publications
Yu Cheng
89 publications
Xin Liu
88 publications
Yi Zhou
80 publications
Ang Li
72 publications
David B. Dunson
71 publications

Fisher Auto-Encoders

It has been conjectured that the Fisher divergence is more robust to mod...
07/12/2020 ∙ by Khalil Elkhalil, et al. ∙ 43 ∙ share

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VarNet: Variational Neural Networks for the Solution of Partial Differential Equations

In this paper we propose a new model-based unsupervised learning method,...
12/16/2019 ∙ by Reza Khodayi-mehr, 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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Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments

We consider three important challenges in conference peer review: (i) re...
06/29/2020 ∙ by Steven Jecmen, et al. ∙ 41 ∙ share

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Toward Automatic Threat Recognition for Airport X-ray Baggage Screening with Deep Convolutional Object Detection

For the safety of the traveling public, the Transportation Security Admi...
12/13/2019 ∙ by Kevin J Liang, et al. ∙ 26 ∙ share

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ParkingSticker: A Real-World Object Detection Dataset

We present a new and challenging object detection dataset, ParkingSticke...
01/31/2020 ∙ by Ethem F. Can, et al. ∙ 26 ∙ share

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Mapping Motor Cortex Stimulation to Muscle Responses: A Deep Neural Network Modeling Approach

A deep neural network (DNN) that can reliably model muscle responses fro...
02/14/2020 ∙ by Md Navid Akbar, et al. ∙ 23 ∙ share

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Generative Adversarial Network Training is a Continual Learning Problem

Generative Adversarial Networks (GANs) have proven to be a powerful fram...
11/27/2018 ∙ by Kevin J Liang, et al. ∙ 22 ∙ share

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DeepObfuscator: Adversarial Training Framework for Privacy-Preserving Image Classification

Deep learning has been widely utilized in many computer vision applicati...
09/09/2019 ∙ by Ang Li, et al. ∙ 21 ∙ share

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Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction

Neural networks are known to be vulnerable to carefully crafted adversar...
11/22/2019 ∙ by Yantao Lu, et al. ∙ 21 ∙ share

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Stochastic Gradient Descent Escapes Saddle Points Efficiently

This paper considers the perturbed stochastic gradient descent algorithm...
02/13/2019 ∙ by Chi Jin, et al. ∙ 20 ∙ share

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How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning

Machine learning has been widely applied to various applications, some o...
04/24/2019 ∙ by Xinlei Pan, et al. ∙ 20 ∙ share

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Towards Understanding Fast Adversarial Training

Current neural-network-based classifiers are susceptible to adversarial ...
06/04/2020 ∙ by Bai Li, et al. ∙ 17 ∙ share

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A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm

In this note, we derive concentration inequalities for random vectors wi...
02/11/2019 ∙ by Chi Jin, et al. ∙ 16 ∙ share

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In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction

In recent years, academics and investigative journalists have criticized...
05/08/2020 ∙ by Caroline Wang, et al. ∙ 16 ∙ share

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LaneNet: Real-Time Lane Detection Networks for Autonomous Driving

Lane detection is to detect lanes on the road and provide the accurate l...
07/04/2018 ∙ by Ze Wang, et al. ∙ 14 ∙ share

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Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization

We improve the robustness of deep neural nets to adversarial attacks by ...
09/23/2018 ∙ by Bao Wang, et al. ∙ 14 ∙ share

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Nonparametric Deconvolution Models

We describe nonparametric deconvolution models (NDMs), a family of Bayes...
03/17/2020 ∙ by Allison J. B. Chaney, et al. ∙ 13 ∙ share

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Model-Based Learning of Turbulent Flows using Mobile Robots

In this paper we consider the problem of model-based learning of turbule...
12/10/2018 ∙ by Reza Khodayi-mehr, et al. ∙ 12 ∙ share

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The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure

There is a stark disparity between the step size schedules used in pract...
04/29/2019 ∙ by Rong Ge, et al. ∙ 12 ∙ share

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A Deep-Learning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images

We consider thyroid-malignancy prediction from ultra-high-resolution who...
04/26/2019 ∙ by David Dov, et al. ∙ 12 ∙ share

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LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets

Federated learning is a popular distributed machine learning paradigm wi...
08/07/2020 ∙ by Ang Li, et al. ∙ 12 ∙ share

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Learning Diverse Fashion Collocation by Neural Graph Filtering

Fashion recommendation systems are highly desired by customers to find v...
03/11/2020 ∙ by Xin Liu, et al. ∙ 11 ∙ share

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DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles

Recent research finds CNN models for image classification demonstrate ov...
09/30/2020 ∙ by Huanrui Yang, et al. ∙ 11 ∙ share

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Survival Function Matching for Calibrated Time-to-Event Predictions

Models for predicting the time of a future event are crucial for risk as...
05/21/2019 ∙ by Paidamoyo Chapfuwa, et al. ∙ 10 ∙ share

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Influence Function based Data Poisoning Attacks to Top-N Recommender Systems

Recommender system is an essential component of web services to engage u...
02/19/2020 ∙ by Minghong Fang, et al. ∙ 10 ∙ share

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Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification

Modern deep neural networks (DNNs) often require high memory consumption...
04/20/2020 ∙ by Huanrui Yang, et al. ∙ 10 ∙ share

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SpiderBoost: A Class of Faster Variance-reduced Algorithms for Nonconvex Optimization

There has been extensive research on developing stochastic variance redu...
10/25/2018 ∙ by Zhe Wang, et al. ∙ 9 ∙ share

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Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions

Human-motion generation is a long-standing challenging task due to the r...
12/21/2019 ∙ by Zhenyi Wang, et al. ∙ 9 ∙ share

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Geometric Matrix Completion with Deep Conditional Random Fields

The problem of completing high-dimensional matrices from a limited set o...
01/29/2019 ∙ by Duc Minh Nguyen, et al. ∙ 8 ∙ share

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Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation

Generating long and semantic-coherent reports to describe medical images...
03/25/2019 ∙ by Christy Y. Li, et al. ∙ 8 ∙ share

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TRP: Trained Rank Pruning for Efficient Deep Neural Networks

To enable DNNs on edge devices like mobile phones, low-rank approximatio...
04/30/2020 ∙ by Yuhui Xu, et al. ∙ 8 ∙ share

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Improved Design of Quadratic Discriminant Analysis Classifier in Unbalanced Settings

The use of quadratic discriminant analysis (QDA) or its regularized vers...
06/11/2020 ∙ by Amine Bejaoui, et al. ∙ 8 ∙ share

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Low to High Dimensional Modality Hallucination using Aggregated Fields of View

Real-world robotics systems deal with data from a multitude of modalitie...
07/13/2020 ∙ by Kausic Gunasekar, et al. ∙ 8 ∙ share

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NTIRE 2020 Challenge on Image and Video Deblurring

Motion blur is one of the most common degradation artifacts in dynamic s...
05/04/2020 ∙ by Seungjun Nah, et al. ∙ 7 ∙ share

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Cooperative Multi-Agent Reinforcement Learning with Partial Observations

In this paper, we propose a distributed zeroth-order policy optimization...
06/18/2020 ∙ by Yan Zhang, et al. ∙ 7 ∙ share

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This looks like that: deep learning for interpretable image recognition

When we are faced with challenging image classification tasks, we often ...
06/27/2018 ∙ by Chaofan Chen, et al. ∙ 6 ∙ share

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Expression of Fractals Through Neural Network Functions

To help understand the underlying mechanisms of neural networks (NNs), s...
05/27/2019 ∙ by Nadav Dym, et al. ∙ 6 ∙ share

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Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors

Deep neural networks (DNNs) are notorious for their vulnerability to adv...
09/15/2019 ∙ by Gilad Cohen, et al. ∙ 6 ∙ share

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Discretized Bottleneck in VAE: Posterior-Collapse-Free Sequence-to-Sequence Learning

Variational autoencoders (VAEs) are important tools in end-to-end repres...
04/22/2020 ∙ by fcq, et al. ∙ 6 ∙ share

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Straight-Through Estimator as Projected Wasserstein Gradient Flow

The Straight-Through (ST) estimator is a widely used technique for back-...
10/05/2019 ∙ by Pengyu Cheng, et al. ∙ 5 ∙ share

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POINTER: Constrained Text Generation via Insertion-based Generative Pre-training

Large-scale pre-trained language models, such as BERT and GPT-2, have ac...
05/01/2020 ∙ by Yizhe Zhang, et al. ∙ 5 ∙ share

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Improving Disentangled Text Representation Learning with Information-Theoretic Guidance

Learning disentangled representations of natural language is essential f...
06/01/2020 ∙ by Pengyu Cheng, et al. ∙ 5 ∙ share

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Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images

We consider preoperative prediction of thyroid cancer based on ultra-hig...
03/29/2019 ∙ by David Dov, et al. ∙ 4 ∙ share

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Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets

Mode connectivity is a surprising phenomenon in the loss landscape of de...
06/14/2019 ∙ by Rohith Kuditipudi, et al. ∙ 3 ∙ share

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TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework with Anonymized Intermediate Representations

The success of deep learning partially benefits from the availability of...
05/23/2020 ∙ by Ang Li, et al. ∙ 3 ∙ share

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MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones

Recent research has made great progress in realizing neural style transf...
05/24/2020 ∙ by Ang Li, et al. ∙ 3 ∙ share

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AdvDetPatch: Attacking Object Detectors with Adversarial Patches

Object detectors have witnessed great progress in recent years and have ...
06/05/2018 ∙ by Xin Liu, et al. ∙ 2 ∙ share

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Solving Jigsaw Puzzles By The Graph Connection Laplacian

We propose a novel mathematical framework to address the problem of auto...
11/07/2018 ∙ by Vahan Huroyan, et al. ∙ 2 ∙ share

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Scalable Thompson Sampling via Optimal Transport

Thompson sampling (TS) is a class of algorithms for sequential decision-...
02/19/2019 ∙ by Ruiyi Zhang, et al. ∙ 2 ∙ share

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