Abhishek Kumar

research

∙ 07/23/2021

Wavelet Design in a Learning Framework

Wavelets have proven to be highly successful in several signal and image...

0 Dhruv Jawali, et al. ∙

research

∙ 07/23/2021

Implicit Rate-Constrained Optimization of Non-decomposable Objectives

We consider a popular family of constrained optimization problems arisin...

0 Abhishek Kumar, et al. ∙

research

∙ 07/13/2021

Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval

We present Retrieve in Style (RIS), an unsupervised framework for fine-g...

2 Min Jin Chong, et al. ∙

research

∙ 07/06/2021

SOCluster- Towards Intent-based Clustering of Stack Overflow Questions using Graph-Based Approach

Stack Overflow (SO) platform has a huge dataset of questions and answers...

0 Abhishek Kumar, et al. ∙

research

∙ 04/17/2021

Risk score learning for COVID-19 contact tracing apps

Digital contact tracing apps for COVID-19, such as the one developed by ...

0 Kevin Murphy, et al. ∙

research

∙ 11/18/2020

Adaptive Contention Window Design using Deep Q-learning

We study the problem of adaptive contention window (CW) design for rando...

0 Abhishek Kumar, et al. ∙

research

∙ 10/22/2020

Few-Shot Adaptation of Generative Adversarial Networks

Generative Adversarial Networks (GANs) have shown remarkable performance...

5 Esther Robb, et al. ∙

research

∙ 06/15/2020

Generalized Adversarially Learned Inference

Allowing effective inference of latent vectors while training GANs can g...

0 Yatin Dandi, et al. ∙

research

∙ 04/25/2020

MCQA: Multimodal Co-attention Based Network for Question Answering

We present MCQA, a learning-based algorithm for multimodal question answ...

0 Abhishek Kumar, et al. ∙

research

∙ 03/03/2020

Marketplace for AI Models

Artificial intelligence shows promise for solving many practical societa...

11 Abhishek Kumar, et al. ∙

research

∙ 02/20/2020

Regularized Autoencoders via Relaxed Injective Probability Flow

Invertible flow-based generative models are an effective method for lear...

5 Abhishek Kumar, et al. ∙

research

∙ 01/31/2020

On Implicit Regularization in β-VAEs

While the impact of variational inference (VI) on posterior inference in...

9 Abhishek Kumar, et al. ∙

research

∙ 01/30/2020

Trustworthy AI in the Age of Pervasive Computing and Big Data

The era of pervasive computing has resulted in countless devices that co...

0 Abhishek Kumar, et al. ∙

research

∙ 12/08/2019

Nonparametric Bayesian Structure Adaptation for Continual Learning

Continual Learning is a learning paradigm where machine learning models ...

0 Abhishek Kumar, et al. ∙

research

∙ 11/28/2019

Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis

In this paper, we propose a two-layered multi-task attention based neura...

0 Abhishek Kumar, et al. ∙

research

∙ 11/11/2019

Customized video filtering on YouTube

Inappropriate and profane content on social media is exponentially incre...

0 Vishal Anand, et al. ∙

research

∙ 10/22/2019

Weakly Supervised Disentanglement with Guarantees

Learning disentangled representations that correspond to factors of vari...

12 Rui Shu, et al. ∙

research

∙ 09/17/2019

Refined α-Divergence Variational Inference via Rejection Sampling

We present an approximate inference method, based on a synergistic combi...

0 Rahul Sharma, et al. ∙

research

∙ 02/06/2019

A Scale Invariant Flatness Measure for Deep Network Minima

It has been empirically observed that the flatness of minima obtained fr...

0 Akshay Rangamani, et al. ∙

research

∙ 11/30/2018

Understanding Unequal Gender Classification Accuracy from Face Images

Recent work shows unequal performance of commercial face classification ...

0 Vidya Muthukumar, et al. ∙

research

∙ 11/21/2018

SpotTune: Transfer Learning through Adaptive Fine-tuning

Transfer learning, which allows a source task to affect the inductive bi...

10 Yunhui Guo, et al. ∙

research

∙ 11/14/2018

MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction

Developing accurate, transferable and computationally inexpensive machin...

12 Soumya Sanyal, et al. ∙

research

∙ 11/13/2018

Co-regularized Alignment for Unsupervised Domain Adaptation

Deep neural networks, trained with large amount of labeled data, can fai...

0 Abhishek Kumar, et al. ∙

research

∙ 06/12/2018

Delta-encoder: an effective sample synthesis method for few-shot object recognition

Learning to classify new categories based on just one or a few examples ...

0 Eli Schwartz, et al. ∙

research

∙ 06/12/2018

RepMet: Representative-based metric learning for classification and one-shot object detection

Distance metric learning (DML) has been successfully applied to object c...

0 Eli Schwartz, et al. ∙

research

∙ 05/20/2018

Knowledge-enriched Two-layered Attention Network for Sentiment Analysis

We propose a novel two-layered attention network based on Bidirectional ...

0 Abhishek Kumar, et al. ∙

research

∙ 11/26/2017

Improved Neural Text Attribute Transfer with Non-parallel Data

Text attribute transfer using non-parallel data requires methods that ca...

0 Igor Melnyk, et al. ∙

research

∙ 11/22/2017

BlockDrop: Dynamic Inference Paths in Residual Networks

Very deep convolutional neural networks offer excellent recognition resu...

0 Zuxuan Wu, et al. ∙

research

∙ 11/21/2017

The Riemannian Geometry of Deep Generative Models

Deep generative models learn a mapping from a low dimensional latent spa...

0 Hang Shao, et al. ∙

research

∙ 11/02/2017

Variational Inference of Disentangled Latent Concepts from Unlabeled Observations

Disentangled representations, where the higher level data generative fac...

0 Abhishek Kumar, et al. ∙

research

∙ 08/01/2017

SenGen: Sentence Generating Neural Variational Topic Model

We present a new topic model that generates documents by sampling a topi...

0 Ramesh Nallapati, et al. ∙

research

∙ 05/24/2017

Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference

Semi-supervised learning methods using Generative Adversarial Networks (...

0 Abhishek Kumar, et al. ∙

research

∙ 11/16/2016

Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification

Multi-task learning aims to improve generalization performance of multip...

0 Yongxi Lu, et al. ∙

research

∙ 11/16/2016

S3Pool: Pooling with Stochastic Spatial Sampling

Feature pooling layers (e.g., max pooling) in convolutional neural netwo...

0 Shuangfei Zhai, et al. ∙

research

∙ 11/03/2016

Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear

To use deep reinforcement learning in the wild, we might hope for an age...

0 Zachary C Lipton, et al. ∙

research

∙ 10/27/2014

Exact and Heuristic Algorithms for Semi-Nonnegative Matrix Factorization

Given a matrix M (not necessarily nonnegative) and a factorization rank ...

0 Nicolas Gillis, et al. ∙

research

∙ 12/27/2013

Near-separable Non-negative Matrix Factorization with ℓ_1- and Bregman Loss Functions

Recently, a family of tractable NMF algorithms have been proposed under ...

0 Abhishek Kumar, et al. ∙

research

∙ 12/13/2012

Identification of Nonlinear Systems From the Knowledge Around Different Operating Conditions: A Feed-Forward Multi-Layer ANN Based Approach

The paper investigates nonlinear system identification using system outp...

0 Sayan Saha, et al. ∙

research

∙ 10/03/2012

Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization

The separability assumption (Donoho & Stodden, 2003; Arora et al., 2012)...

0 Abhishek Kumar, et al. ∙

research

∙ 06/27/2012

A Binary Classification Framework for Two-Stage Multiple Kernel Learning

With the advent of kernel methods, automating the task of specifying a s...

0 Abhishek Kumar, et al. ∙

research

∙ 06/27/2012

Learning Task Grouping and Overlap in Multi-task Learning

In the paradigm of multi-task learning, mul- tiple related prediction ta...

0 Abhishek Kumar, et al. ∙

Abhishek Kumar

Featured Co-authors