Vikas Singh

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University of Wisconsin-Madison

Associate Professor in the Department of Biostatistics and the Department of Computer Sciences at the University of Wisconsin-Madison

Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators

We study how stochastic differential equation (SDE) based ideas can insp...
08/19/2021 ∙ by Zihang Meng, et al. ∙ 0 ∙ share

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An Online Riemannian PCA for Stochastic Canonical Correlation Analysis

We present an efficient stochastic algorithm (RSG+) for canonical correl...
06/08/2021 ∙ by Zihang Meng, et al. ∙ 0 ∙ share

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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...
05/12/2021 ∙ by Zihang Meng, et al. ∙ 9 ∙ share

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Simpler Certified Radius Maximization by Propagating Covariances

One strategy for adversarially training a robust model is to maximize it...
04/13/2021 ∙ by Xingjian Zhen, et al. ∙ 0 ∙ share

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Learning Invariant Representations using Inverse Contrastive Loss

Learning invariant representations is a critical first step in a number ...
02/16/2021 ∙ by Aditya Kumar Akash, et al. ∙ 0 ∙ share

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

Transformers have emerged as a powerful tool for a broad range of natura...
02/07/2021 ∙ by Yunyang Xiong, et al. ∙ 0 ∙ share

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

Customer satisfaction is an important factor in creating and maintaining...
01/31/2021 ∙ by Teja Kanchinadam, et al. ∙ 0 ∙ share

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Flow-based Generative Models for Learning Manifold to Manifold Mappings

Many measurements or observations in computer vision and machine learnin...
12/18/2020 ∙ by Xingjian Zhen, et al. ∙ 17 ∙ share

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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...
09/02/2020 ∙ by Vikas Singh, et al. ∙ 9 ∙ share

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Online Graph Completion: Multivariate Signal Recovery in Computer Vision

The adoption of "human-in-the-loop" paradigms in computer vision and mac...
08/12/2020 ∙ by Won Hwa Kim, et al. ∙ 3 ∙ share

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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...
08/10/2020 ∙ by Vikas Singh, et al. ∙ 7 ∙ share

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Physarum Powered Differentiable Linear Programming Layers and Applications

Consider a learning algorithm, which involves an internal call to an opt...
04/30/2020 ∙ by Zihang Meng, et al. ∙ 5 ∙ share

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MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

Inverted bottleneck layers, which are built upon depthwise convolutions,...
04/30/2020 ∙ by Yunyang Xiong, et al. ∙ 15 ∙ share

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

Algorithmic decision making based on computer vision and machine learnin...
04/03/2020 ∙ by Vishnu Suresh Lokhande, et al. ∙ 0 ∙ share

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

In recent years, intelligent condition-based monitoring of rotary machin...
12/24/2019 ∙ by Vikas Singh, et al. ∙ 12 ∙ share

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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 ...
12/24/2019 ∙ by Vikas Singh, et al. ∙ 43 ∙ share

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Dilated Convolutional Neural Networks for Sequential Manifold-valued Data

Efforts are underway to study ways via which the power of deep neural ne...
10/05/2019 ∙ by Xingjian Zhen, et al. ∙ 0 ∙ share

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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...
09/26/2019 ∙ by Sathya N. Ravi, et al. ∙ 17 ∙ share

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Generating Accurate Pseudo-labels via Hermite Polynomials for SSL Confidently

Rectified Linear Units (ReLUs) are among the most widely used activation...
09/12/2019 ∙ by Vishnu Suresh Lokhande, et al. ∙ 0 ∙ share

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

Positron emission tomography (PET) imaging is an imaging modality for di...
08/21/2019 ∙ by Haoliang Sun, et al. ∙ 12 ∙ share

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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...
08/20/2019 ∙ by TIen Vo, et al. ∙ 0 ∙ share

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Fooling Computer Vision into Inferring the Wrong Body Mass Index

Recently it's been shown that neural networks can use images of human fa...
05/16/2019 ∙ by Owen Levin, et al. ∙ 0 ∙ share

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Resource Constrained Neural Network Architecture Search

The design of neural network architectures is frequently either based on...
04/08/2019 ∙ by Yunyang Xiong, et al. ∙ 0 ∙ share

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

Generative models using neural network have opened a door to large-scale...
11/24/2018 ∙ by Seong Jae Hwang, et al. ∙ 0 ∙ share

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

We provide simple schemes to build Bayesian Neural Networks (BNNs), bloc...
06/10/2018 ∙ by Hao Henry Zhou, et al. ∙ 0 ∙ share

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Statistical Recurrent Models on Manifold valued Data

In a number of disciplines, the data (e.g., graphs, manifolds) to be ana...
05/29/2018 ∙ by Rudrasis Chakraborty, et al. ∙ 0 ∙ share

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

There has recently been a concerted effort to derive mechanisms in visio...
04/19/2018 ∙ by Seong Jae Hwang, et al. ∙ 0 ∙ share

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Robust Blind Deconvolution via Mirror Descent

We revisit the Blind Deconvolution problem with a focus on understanding...
03/21/2018 ∙ by Sathya N. Ravi, et al. ∙ 0 ∙ share

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Constrained Deep Learning using Conditional Gradient and Applications in Computer Vision

A number of results have recently demonstrated the benefits of incorpora...
03/17/2018 ∙ by Sathya N. Ravi, et al. ∙ 0 ∙ share

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

Recent results in coupled or temporal graphical models offer schemes for...
11/20/2017 ∙ by Ronak Mehta, et al. ∙ 0 ∙ share

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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...
09/02/2017 ∙ by Hao Henry Zhou, et al. ∙ 0 ∙ share

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A Deterministic Nonsmooth Frank Wolfe Algorithm with Coreset Guarantees

We present a new Frank-Wolfe (FW) type algorithm that is applicable to m...
08/22/2017 ∙ by Sathya N. Ravi, et al. ∙ 0 ∙ share

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The Incremental Multiresolution Matrix Factorization Algorithm

Multiresolution analysis and matrix factorization are foundational tools...
05/16/2017 ∙ by Vamsi K. Ithapu, et al. ∙ 0 ∙ share

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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...
03/04/2017 ∙ by Felipe Gutierrez-Barragan, et al. ∙ 0 ∙ share

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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...
02/28/2017 ∙ by Vamsi K. Ithapu, et al. ∙ 0 ∙ share

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

The regularization and output consistency behavior of dropout and layer-...
11/17/2015 ∙ by Vamsi K. Ithapu, et al. ∙ 0 ∙ share

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

Unsupervised pretraining and dropout have been well studied, especially ...
06/10/2015 ∙ by Vamsi K. Ithapu, et al. ∙ 0 ∙ share

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Convergence of gradient based pre-training in Denoising autoencoders

The success of deep architectures is at least in part attributed to the ...
02/12/2015 ∙ by Vamsi K. Ithapu, et al. ∙ 0 ∙ share

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Speeding up Permutation Testing in Neuroimaging

Multiple hypothesis testing is a significant problem in nearly all neuro...
02/12/2015 ∙ by Chris Hinrichs, et al. ∙ 0 ∙ share

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Incorporating Domain Knowledge in Matching Problems via Harmonic Analysis

Matching one set of objects to another is a ubiquitous task in machine l...
06/27/2012 ∙ by Deepti Pachauri, et al. ∙ 0 ∙ share

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Vikas Singh

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