b'Vikas Singh'

research

∙ 07/21/2022

Multi Resolution Analysis (MRA) for Approximate Self-Attention

Transformers have emerged as a preferred model for many tasks in natural...

Zhanpeng Zeng, et al. ∙

research

∙ 07/20/2022

On the Versatile Uses of Partial Distance Correlation in Deep Learning

Comparing the functional behavior of neural network models, whether it i...

Xingjian Zhen, et al. ∙

research

∙ 04/15/2022

Deep Unlearning via Randomized Conditionally Independent Hessians

Recent legislation has led to interest in machine unlearning, i.e., remo...

Ronak Mehta, et al. ∙

research

∙ 03/29/2022

Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets

Pooling multiple neuroimaging datasets across institutions often enables...

Vishnu Suresh Lokhande, et al. ∙

research

∙ 02/19/2022

Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks

Uncertainty estimation in deep models is essential in many real-world ap...

Jurijs Nazarovs, et al. ∙

research

∙ 02/18/2022

Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data

Panel data involving longitudinal measurements of the same set of partic...

Jurijs Nazarovs, et al. ∙

research

∙ 12/01/2021

Forward Operator Estimation in Generative Models with Kernel Transfer Operators

Generative models which use explicit density modeling (e.g., variational...

Zhichun Huang, et al. ∙

research

∙ 11/18/2021

You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling

Transformer-based models are widely used in natural language processing ...

Zhanpeng Zeng, et al. ∙

research

∙ 08/19/2021

Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators

We study how stochastic differential equation (SDE) based ideas can insp...

Zihang Meng, et al. ∙

research

∙ 06/08/2021

An Online Riemannian PCA for Stochastic Canonical Correlation Analysis

We present an efficient stochastic algorithm (RSG+) for canonical correl...

Zihang Meng, et al. ∙

research

∙ 05/12/2021

Connecting What to Say With Where to Look by Modeling Human Attention Traces

We introduce a unified framework to jointly model images, text, and huma...

Zihang Meng, et al. ∙

research

∙ 04/13/2021

Simpler Certified Radius Maximization by Propagating Covariances

One strategy for adversarially training a robust model is to maximize it...

Xingjian Zhen, et al. ∙

research

∙ 02/16/2021

Learning Invariant Representations using Inverse Contrastive Loss

Learning invariant representations is a critical first step in a number ...

Aditya Kumar Akash, et al. ∙

research

∙ 02/07/2021

Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention

Transformers have emerged as a powerful tool for a broad range of natura...

Yunyang Xiong, et al. ∙

research

∙ 01/31/2021

Graph Neural Networks to Predict Customer Satisfaction Following Interactions with a Corporate Call Center

Customer satisfaction is an important factor in creating and maintaining...

Teja Kanchinadam, et al. ∙

research

∙ 12/18/2020

Flow-based Generative Models for Learning Manifold to Manifold Mappings

Many measurements or observations in computer vision and machine learnin...

Xingjian Zhen, et al. ∙

research

∙ 09/02/2020

A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification

Clustering techniques have been proved highly suc-cessful for Takagi-Sug...

Vikas Singh, et al. ∙

research

∙ 08/12/2020

Online Graph Completion: Multivariate Signal Recovery in Computer Vision

The adoption of "human-in-the-loop" paradigms in computer vision and mac...

Won Hwa Kim, et al. ∙

research

∙ 08/10/2020

Improved Adaptive Type-2 Fuzzy Filter with Exclusively Two Fuzzy Membership Function for Filtering Salt and Pepper Noise

Image denoising is one of the preliminary steps in image processing meth...

Vikas Singh, et al. ∙

research

∙ 04/30/2020

Physarum Powered Differentiable Linear Programming Layers and Applications

Consider a learning algorithm, which involves an internal call to an opt...

Zihang Meng, et al. ∙

research

∙ 04/30/2020

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

Inverted bottleneck layers, which are built upon depthwise convolutions,...

Yunyang Xiong, et al. ∙

research

∙ 04/03/2020

FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret

Algorithmic decision making based on computer vision and machine learnin...

Vishnu Suresh Lokhande, et al. ∙

research

∙ 12/24/2019

mRMR-DNN with Transfer Learning for IntelligentFault Diagnosis of Rotating Machines

In recent years, intelligent condition-based monitoring of rotary machin...

Vikas Singh, et al. ∙

research

∙ 12/24/2019

An Entropy-based Variable Feature Weighted Fuzzy k-Means Algorithm for High Dimensional Data

This paper presents a new fuzzy k-means algorithm for the clustering of ...

Vikas Singh, et al. ∙

research

∙ 10/05/2019

Dilated Convolutional Neural Networks for Sequential Manifold-valued Data

Efforts are underway to study ways via which the power of deep neural ne...

Xingjian Zhen, et al. ∙

research

∙ 09/26/2019

Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization offers Significant Performance and Efficiency Gains

Data dependent regularization is known to benefit a wide variety of prob...

Sathya N. Ravi, et al. ∙

research

∙ 09/12/2019

Generating Accurate Pseudo-labels via Hermite Polynomials for SSL Confidently

Rectified Linear Units (ReLUs) are among the most widely used activation...

Vishnu Suresh Lokhande, et al. ∙

research

∙ 08/21/2019

DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer

Positron emission tomography (PET) imaging is an imaging modality for di...

Haoliang Sun, et al. ∙

research

∙ 08/20/2019

Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data

For large-scale testing with graph-associated data, we present an empiri...

TIen Vo, et al. ∙

research

∙ 05/16/2019

Fooling Computer Vision into Inferring the Wrong Body Mass Index

Recently it's been shown that neural networks can use images of human fa...

Owen Levin, et al. ∙

research

∙ 04/08/2019

Resource Constrained Neural Network Architecture Search

The design of neural network architectures is frequently either based on...

Yunyang Xiong, et al. ∙

research

∙ 11/24/2018

Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging

Generative models using neural network have opened a door to large-scale...

Seong Jae Hwang, et al. ∙

research

∙ 06/10/2018

Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty

We provide simple schemes to build Bayesian Neural Networks (BNNs), bloc...

Hao Henry Zhou, et al. ∙

research

∙ 05/29/2018

Statistical Recurrent Models on Manifold valued Data

In a number of disciplines, the data (e.g., graphs, manifolds) to be ana...

Rudrasis Chakraborty, et al. ∙

research

∙ 04/19/2018

Sampling-free Uncertainty Estimation in Gated Recurrent Units with Exponential Families

There has recently been a concerted effort to derive mechanisms in visio...

Seong Jae Hwang, et al. ∙

research

∙ 03/21/2018

Robust Blind Deconvolution via Mirror Descent

We revisit the Blind Deconvolution problem with a focus on understanding...

Sathya N. Ravi, et al. ∙

research

∙ 03/17/2018

Constrained Deep Learning using Conditional Gradient and Applications in Computer Vision

A number of results have recently demonstrated the benefits of incorpora...

Sathya N. Ravi, et al. ∙

research

∙ 11/20/2017

Finding Differentially Covarying Needles in a Temporally Evolving Haystack: A Scan Statistics Perspective

Recent results in coupled or temporal graphical models offer schemes for...

Ronak Mehta, et al. ∙

research

∙ 09/02/2017

When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, ℓ_2-consistency and Neuroscience Applications

Many studies in biomedical and health sciences involve small sample size...

Hao Henry Zhou, et al. ∙

research

∙ 08/22/2017

A Deterministic Nonsmooth Frank Wolfe Algorithm with Coreset Guarantees

We present a new Frank-Wolfe (FW) type algorithm that is applicable to m...

Sathya N. Ravi, et al. ∙

research

∙ 05/16/2017

The Incremental Multiresolution Matrix Factorization Algorithm

Multiresolution analysis and matrix factorization are foundational tools...

Vamsi K. Ithapu, et al. ∙

research

∙ 03/04/2017

Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM

Permutation testing is a non-parametric method for obtaining the max nul...

Felipe Gutierrez-Barragan, et al. ∙

research

∙ 02/28/2017

On architectural choices in deep learning: From network structure to gradient convergence and parameter estimation

We study mechanisms to characterize how the asymptotic convergence of ba...

Vamsi K. Ithapu, et al. ∙

research

∙ 11/17/2015

On the interplay of network structure and gradient convergence in deep learning

The regularization and output consistency behavior of dropout and layer-...

Vamsi K. Ithapu, et al. ∙

research

∙ 06/10/2015

Convergence rates for pretraining and dropout: Guiding learning parameters using network structure

Unsupervised pretraining and dropout have been well studied, especially ...

Vamsi K. Ithapu, et al. ∙

research

∙ 02/12/2015

Convergence of gradient based pre-training in Denoising autoencoders

The success of deep architectures is at least in part attributed to the ...

Vamsi K. Ithapu, et al. ∙

research

∙ 02/12/2015

Speeding up Permutation Testing in Neuroimaging

Multiple hypothesis testing is a significant problem in nearly all neuro...

Chris Hinrichs, et al. ∙

research

∙ 06/27/2012

Incorporating Domain Knowledge in Matching Problems via Harmonic Analysis

Matching one set of objects to another is a ubiquitous task in machine l...

Deepti Pachauri, et al. ∙

Vikas Singh

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