Lawrence Carin

research

∙ 03/09/2023

Open World Classification with Adaptive Negative Samples

Open world classification is a task in natural language processing with ...

0 Ke Bai, et al. ∙

research

∙ 10/23/2022

Pushing the Efficiency Limit Using Structured Sparse Convolutions

Weight pruning is among the most popular approaches for compressing deep...

0 Vinay Kumar Verma, et al. ∙

research

∙ 10/17/2022

Pseudo-OOD training for robust language models

While pre-trained large-scale deep models have garnered attention as an ...

0 Dhanasekar Sundararaman, et al. ∙

research

∙ 09/20/2022

Collaborative Anomaly Detection

In recommendation systems, items are likely to be exposed to various use...

0 Ke Bai, et al. ∙

research

∙ 05/07/2022

Number Entity Recognition

Numbers are essential components of text, like any other word tokens, fr...

0 Dhanasekar Sundararaman, et al. ∙

research

∙ 02/25/2022

Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations

End-to-end learning of dynamical systems with black-box models, such as ...

5 Paidamoyo Chapfuwa, et al. ∙

research

∙ 11/24/2021

Explainable multiple abnormality classification of chest CT volumes with AxialNet and HiResCAM

Understanding model predictions is critical in healthcare, to facilitate...

0 Rachel Lea Ballantyne Draelos, et al. ∙

research

∙ 11/04/2021

Finite-Time Consensus Learning for Decentralized Optimization with Nonlinear Gossiping

Distributed learning has become an integral tool for scaling up machine ...

0 Junya Chen, et al. ∙

research

∙ 11/04/2021

Variational Inference with Holder Bounds

The recent introduction of thermodynamic integration techniques has prov...

0 Junya Chen, et al. ∙

research

∙ 07/09/2021

Hölder Bounds for Sensitivity Analysis in Causal Reasoning

We examine interval estimation of the effect of a treatment T on an outc...

0 Serge Assaad, et al. ∙

research

∙ 07/05/2021

Imputation-Free Learning from Incomplete Observations

Although recent works have developed methods that can generate estimatio...

0 Qitong Gao, et al. ∙

research

∙ 04/27/2021

Towards Fair Federated Learning with Zero-Shot Data Augmentation

Federated learning has emerged as an important distributed learning para...

7 Weituo Hao, et al. ∙

research

∙ 04/02/2021

Malignancy Prediction and Lesion Identification from Clinical Dermatological Images

We consider machine-learning-based malignancy prediction and lesion iden...

0 Meng Xia, et al. ∙

research

∙ 03/25/2021

Efficient Feature Transformations for Discriminative and Generative Continual Learning

As neural networks are increasingly being applied to real-world applicat...

0 Vinay Kumar Verma, et al. ∙

research

∙ 03/17/2021

Improving Zero-shot Voice Style Transfer via Disentangled Representation Learning

Voice style transfer, also called voice conversion, seeks to modify one ...

0 Siyang Yuan, et al. ∙

research

∙ 03/11/2021

FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders

Pretrained text encoders, such as BERT, have been applied increasingly i...

17 Pengyu Cheng, et al. ∙

research

∙ 03/06/2021

Efficient Continual Adaptation for Generative Adversarial Networks

We present a continual learning approach for generative adversarial netw...

0 Sakshi Varshney, et al. ∙

research

∙ 02/23/2021

Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning

Zero-shot learning (ZSL) has been shown to be a promising approach to ge...

0 Vinay Kumar Verma, et al. ∙

research

∙ 02/10/2021

FLOP: Federated Learning on Medical Datasets using Partial Networks

The outbreak of COVID-19 Disease due to the novel coronavirus has caused...

0 Qian Yang, et al. ∙

research

∙ 01/17/2021

What Makes Good In-Context Examples for GPT-3?

GPT-3 has attracted lots of attention due to its superior performance ac...

0 Jiachang Liu, et al. ∙

research

∙ 12/15/2020

Wasserstein Contrastive Representation Distillation

The primary goal of knowledge distillation (KD) is to encapsulate the in...

0 Liqun Chen, et al. ∙

research

∙ 12/10/2020

Learning Graphons via Structured Gromov-Wasserstein Barycenters

We propose a novel and principled method to learn a nonparametric graph ...

0 Hongteng Xu, et al. ∙

research

∙ 12/06/2020

Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models

Deep neural networks have shown significant promise in comprehending com...

11 Dong Wang, et al. ∙

research

∙ 11/25/2020

Supercharging Imbalanced Data Learning With Causal Representation Transfer

Dealing with severe class imbalance poses a major challenge for real-wor...

0 Junya Chen, et al. ∙

research

∙ 11/17/2020

HiResCAM: Explainable Multi-Organ Multi-Abnormality Prediction in 3D Medical Images

Understanding model predictions is critical in healthcare, to facilitate...

14 Rachel Lea Ballantyne Draelos, et al. ∙

research

∙ 11/09/2020

Estimating Total Correlation with Mutual Information Bounds

Total correlation (TC) is a fundamental concept in information theory to...

0 Pengyu Cheng, et al. ∙

research

∙ 11/01/2020

MixKD: Towards Efficient Distillation of Large-scale Language Models

Large-scale language models have recently demonstrated impressive empiri...

0 Kevin J Liang, et al. ∙

research

∙ 10/23/2020

Counterfactual Representation Learning with Balancing Weights

A key to causal inference with observational data is achieving balance i...

0 Serge Assaad, et al. ∙

research

∙ 10/15/2020

Double Robust Representation Learning for Counterfactual Prediction

Causal inference, or counterfactual prediction, is central to decision m...

11 Shuxi Zeng, et al. ∙

research

∙ 10/15/2020

RetiNerveNet: Using Recursive Deep Learning to Estimate Pointwise 24-2 Visual Field Data based on Retinal Structure

Glaucoma is the leading cause of irreversible blindness in the world, af...

0 Shounak Datta, et al. ∙

research

∙ 10/12/2020

Improving Text Generation with Student-Forcing Optimal Transport

Neural language models are often trained with maximum likelihood estimat...

2 Guoyin Wang, et al. ∙

research

∙ 10/02/2020

Background Adaptive Faster R-CNN for Semi-Supervised Convolutional Object Detection of Threats in X-Ray Images

Recently, progress has been made in the supervised training of Convoluti...

0 John B. Sigman, et al. ∙

research

∙ 08/14/2020

Weakly supervised cross-domain alignment with optimal transport

Cross-domain alignment between image objects and text sequences is key t...

9 Siyang Yuan, et al. ∙

research

∙ 08/13/2020

WAFFLe: Weight Anonymized Factorization for Federated Learning

In domains where data are sensitive or private, there is great value in ...

16 Weituo Hao, et al. ∙

research

∙ 07/13/2020

Bridging Maximum Likelihood and Adversarial Learning via α-Divergence

Maximum likelihood (ML) and adversarial learning are two popular approac...

1 Miaoyun Zhao, et al. ∙

research

∙ 06/26/2020

Graph Optimal Transport for Cross-Domain Alignment

Cross-domain alignment between two sets of entities (e.g., objects in an...

9 Liqun Chen, et al. ∙

research

∙ 06/22/2020

CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information

Mutual information (MI) minimization has gained considerable interests i...

38 Pengyu Cheng, et al. ∙

research

∙ 06/16/2020

GO Hessian for Expectation-Based Objectives

An unbiased low-variance gradient estimator, termed GO gradient, was pro...

1 Yulai Cong, et al. ∙

research

∙ 06/14/2020

Survival Analysis meets Counterfactual Inference

There is growing interest in applying machine learning methods for count...

0 Paidamoyo Chapfuwa, et al. ∙

research

∙ 06/13/2020

GAN Memory with No Forgetting

Seeking to address the fundamental issue of memory in lifelong learning,...

1 Yulai Cong, et al. ∙

research

∙ 06/12/2020

Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization

Control variates are a well-established tool to reduce the variance of M...

0 Shijing Si, et al. ∙

research

∙ 06/04/2020

Hierarchical Optimal Transport for Robust Multi-View Learning

Traditional multi-view learning methods often rely on two assumptions: (...

0 Dixin Luo, et al. ∙

research

∙ 06/04/2020

Towards Understanding Fast Adversarial Training

Current neural-network-based classifiers are susceptible to adversarial ...

17 Bai Li, et al. ∙

research

∙ 06/01/2020

Improving Disentangled Text Representation Learning with Information-Theoretic Guidance

Learning disentangled representations of natural language is essential f...

5 Pengyu Cheng, et al. ∙

research

∙ 05/08/2020

Y-Net for Chest X-Ray Preprocessing: Simultaneous Classification of Geometry and Segmentation of Annotations

Over the last decade, convolutional neural networks (CNNs) have emerged ...

0 John McManigle, et al. ∙

research

∙ 05/04/2020

Reward Constrained Interactive Recommendation with Natural Language Feedback

Text-based interactive recommendation provides richer user feedback and ...

0 Ruiyi Zhang, et al. ∙

research

∙ 05/04/2020

Improving Adversarial Text Generation by Modeling the Distant Future

Auto-regressive text generation models usually focus on local fluency, a...

0 Ruiyi Zhang, et al. ∙

research

∙ 04/30/2020

APo-VAE: Text Generation in Hyperbolic Space

Natural language often exhibits inherent hierarchical structure ingraine...

0 Shuyang Dai, et al. ∙

research

∙ 04/29/2020

Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability

We consider the blackbox transfer-based targeted adversarial attack thre...

0 Nathan Inkawhich, et al. ∙

research

∙ 04/27/2020

Transferable Perturbations of Deep Feature Distributions

Almost all current adversarial attacks of CNN classifiers rely on inform...

0 Nathan Inkawhich, et al. ∙

Lawrence Carin

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