
Physarum Powered Differentiable Linear Programming Layers and Applications
Consider a learning algorithm, which involves an internal call to an opt...
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MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
Inverted bottleneck layers, which are built upon depthwise convolutions,...
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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...
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mRMRDNN with Transfer Learning for IntelligentFault Diagnosis of Rotating Machines
In recent years, intelligent conditionbased monitoring of rotary machin...
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An Entropybased Variable Feature Weighted Fuzzy kMeans Algorithm for High Dimensional Data
This paper presents a new fuzzy kmeans algorithm for the clustering of ...
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Dilated Convolutional Neural Networks for Sequential Manifoldvalued Data
Efforts are underway to study ways via which the power of deep neural ne...
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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...
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Generating Accurate Pseudolabels via Hermite Polynomials for SSL Confidently
Rectified Linear Units (ReLUs) are among the most widely used activation...
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DUALGLOW: Conditional FlowBased Generative Model for Modality Transfer
Positron emission tomography (PET) imaging is an imaging modality for di...
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Dimension constraints improve hypothesis testing for largescale, graphassociated, brainimage data
For largescale testing with graphassociated data, we present an empiri...
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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...
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Resource Constrained Neural Network Architecture Search
The design of neural network architectures is frequently either based on...
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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 largescale...
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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...
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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...
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Samplingfree Uncertainty Estimation in Gated Recurrent Units with Exponential Families
There has recently been a concerted effort to derive mechanisms in visio...
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Robust Blind Deconvolution via Mirror Descent
We revisit the Blind Deconvolution problem with a focus on understanding...
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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...
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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...
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When can MultiSite Datasets be Pooled for Regression? Hypothesis Tests, ℓ_2consistency and Neuroscience Applications
Many studies in biomedical and health sciences involve small sample size...
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A Deterministic Nonsmooth Frank Wolfe Algorithm with Coreset Guarantees
We present a new FrankWolfe (FW) type algorithm that is applicable to m...
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The Incremental Multiresolution Matrix Factorization Algorithm
Multiresolution analysis and matrix factorization are foundational tools...
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Accelerating Permutation Testing in Voxelwise Analysis through Subspace Tracking: A new plugin for SnPM
Permutation testing is a nonparametric method for obtaining the max nul...
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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...
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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...
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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 ...
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Convergence of gradient based pretraining in Denoising autoencoders
The success of deep architectures is at least in part attributed to the ...
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Speeding up Permutation Testing in Neuroimaging
Multiple hypothesis testing is a significant problem in nearly all neuro...
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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...
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Vikas Singh
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Associate Professor in the Department of Biostatistics and the Department of Computer Sciences at the University of WisconsinMadison