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ORCIDVikas Singh
This is just a disambiguation page, and is not intended to be the bibliography of an actual person. Any publication listed on this page has not been assigned to an actual author yet. If you know the true author of one of the publications listed below, you are welcome to contact us.
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2020 – today
- 2022
[j30]Tuan Q. Dinh
, Yunyang Xiong, Zhichun Huang, Tien Vo, Akshay Mishra, Won Hwa Kim, Sathya N. Ravi, Vikas Singh:
Performing Group Difference Testing on Graph Structured Data From GANs: Analysis and Applications in Neuroimaging. IEEE Trans. Pattern Anal. Mach. Intell. 44(2): 877-889 (2022)- [c118]Zhichun Huang, Rudrasis Chakraborty, Vikas Singh:
Forward Operator Estimation in Generative Models with Kernel Transfer Operators. ICML 2022: 9148-9172 - [c117]Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn Moo Fung, Vikas Singh:
Multi Resolution Analysis (MRA) for Approximate Self-Attention. ICML 2022: 25955-25972 - [c116]Anita Sinha, Ronak Mehta, Veena A. Nair, Rasmus M. Birn, Vikas Singh, Vivek Prabhakaran:
Investigating Functional Brain Network Abnormalities via Differential Covariance Trajectory Analysis and Scan Statistics. ISBI 2022: 1-4 - [i45]Jurijs Nazarovs, Rudrasis Chakraborty, Songwong Tasneeyapant, Sathya N. Ravi, Vikas Singh:
Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data. CoRR abs/2202.09463 (2022) - [i44]Jurijs Nazarovs, Ronak R. Mehta, Vishnu Suresh Lokhande, Vikas Singh:
Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks. CoRR abs/2202.09478 (2022) - [i43]Vishnu Suresh Lokhande, Rudrasis Chakraborty, Sathya N. Ravi, Vikas Singh:
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets. CoRR abs/2203.15234 (2022) - [i42]Ronak Mehta, Sourav Pal, Vikas Singh, Sathya N. Ravi:
Deep Unlearning via Randomized Conditionally Independent Hessians. CoRR abs/2204.07655 (2022) - [i41]Xingjian Zhen, Zihang Meng, Rudrasis Chakraborty, Vikas Singh:
On the Versatile Uses of Partial Distance Correlation in Deep Learning. CoRR abs/2207.09684 (2022) - [i40]Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn Moo Fung, Vikas Singh:
Multi Resolution Analysis (MRA) for Approximate Self-Attention. CoRR abs/2207.10284 (2022) - 2021
- [j29]Nishchal K. Verma, Teena Sharma, Sonal Dixit, Pooja Agrawal, Sourya Sengupta, Vikas Singh:
BIDEAL: A Toolbox for Bicluster Analysis - Generation, Visualization and Validation. SN Comput. Sci. 2(1): 24 (2021) - [j28]Seetaram Maurya, Vikas Singh, Nishchal K. Verma, Chris K. Mechefske:
Condition-Based Monitoring in Variable Machine Running Conditions Using Low-Level Knowledge Transfer With DNN. IEEE Trans Autom. Sci. Eng. 18(4): 1983-1997 (2021) - [c115]Aditya Kumar Akash, Vishnu Suresh Lokhande, Sathya N. Ravi, Vikas Singh:
Learning Invariant Representations using Inverse Contrastive Loss. AAAI 2021: 6582-6591 - [c114]Zihang Meng, Sathya N. Ravi, Vikas Singh:
Physarum Powered Differentiable Linear Programming Layers and Applications. AAAI 2021: 8939-8949 - [c113]Xingjian Zhen, Rudrasis Chakraborty, Liu Yang, Vikas Singh:
Flow-based Generative Models for Learning Manifold to Manifold Mappings. AAAI 2021: 11042-11052 - [c112]Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh:
Nyströmformer: A Nyström-based Algorithm for Approximating Self-Attention. AAAI 2021: 14138-14148 - [c111]Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Yongzhe Wang, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen:
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators. CVPR 2021: 3825-3834 - [c110]Xingjian Zhen, Rudrasis Chakraborty, Vikas Singh:
Simpler Certified Radius Maximization by Propagating Covariances. CVPR 2021: 7292-7301 - [c109]Zihang Meng, Licheng Yu, Ning Zhang, Tamara L. Berg, Babak Damavandi, Vikas Singh, Amy Bearman:
Connecting What To Say With Where To Look by Modeling Human Attention Traces. CVPR 2021: 12679-12688 - [c108]Zihang Meng, Vikas Singh, Sathya N. Ravi:
Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators. ICCV 2021: 11615-11624 - [c107]Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Moo Fung, Vikas Singh:
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling. ICML 2021: 12321-12332 - [c106]Zihang Meng, Rudrasis Chakraborty, Vikas Singh:
An Online Riemannian PCA for Stochastic Canonical Correlation Analysis. NeurIPS 2021: 14056-14068 - [c105]Zihang Meng, Lopamudra Mukherjee, Yichao Wu, Vikas Singh, Sathya N. Ravi:
Differentiable Optimization of Generalized Nondecomposable Functions using Linear Programs. NeurIPS 2021: 29129-29141 - [c104]Jurijs Nazarovs, Rudrasis Chakraborty, Songwong Tasneeyapant, Sathya N. Ravi, Vikas Singh:
A variational approximation for analyzing the dynamics of panel data. UAI 2021: 107-117 - [c103]Jurijs Nazarovs, Ronak R. Mehta, Vishnu Suresh Lokhande, Vikas Singh:
Graph reparameterizations for enabling 1000+ Monte Carlo iterations in Bayesian deep neural networks. UAI 2021: 118-128 - [i39]Teja Kanchinadam, Zihang Meng, Joseph Bockhorst, Vikas Singh, Glenn Fung:
Graph Neural Networks to Predict Customer Satisfaction Following Interactions with a Corporate Call Center. CoRR abs/2102.00420 (2021) - [i38]Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh:
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention. CoRR abs/2102.03902 (2021) - [i37]Aditya Kumar Akash, Vishnu Suresh Lokhande, Sathya N. Ravi, Vikas Singh:
Learning Invariant Representations using Inverse Contrastive Loss. CoRR abs/2102.08343 (2021) - [i36]Xingjian Zhen, Rudrasis Chakraborty, Vikas Singh:
Simpler Certified Radius Maximization by Propagating Covariances. CoRR abs/2104.05888 (2021) - [i35]Zihang Meng, Licheng Yu, Ning Zhang, Tamara L. Berg, Babak Damavandi, Vikas Singh, Amy Bearman:
Connecting What to Say With Where to Look by Modeling Human Attention Traces. CoRR abs/2105.05964 (2021) - [i34]Zihang Meng, Rudrasis Chakraborty, Vikas Singh:
An Online Riemannian PCA for Stochastic Canonical Correlation Analysis. CoRR abs/2106.07479 (2021) - [i33]Zihang Meng, Vikas Singh, Sathya N. Ravi:
Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators. CoRR abs/2108.08891 (2021) - [i32]Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh:
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling. CoRR abs/2111.09714 (2021) - [i31]Zhichun Huang, Rudrasis Chakraborty, Vikas Singh:
Forward Operator Estimation in Generative Models with Kernel Transfer Operators. CoRR abs/2112.00305 (2021) - 2020
- [j27]Arthur F. A. Fernandes, Eduardo M. Turra, Érika R. de Alvarenga, Tiago L. Passafaro, Fernando B. Lopes, Gabriel F. O. Alves, Vikas Singh, Guilherme J. M. Rosa:
Deep Learning image segmentation for extraction of fish body measurements and prediction of body weight and carcass traits in Nile tilapia. Comput. Electron. Agric. 170: 105274 (2020) - [j26]Arun K. Sharma, Dhan Jeet Singh, Vikas Singh, Nishchal K. Verma:
Aerodynamic Modeling of ATTAS Aircraft Using Mamdani Fuzzy Inference Network. IEEE Trans. Aerosp. Electron. Syst. 56(5): 3566-3576 (2020) - [c102]Sathya N. Ravi, Abhay Venkatesh, Glenn Moo Fung, Vikas Singh:
Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization Offers Significant Performance and Efficiency Gains. AAAI 2020: 5487-5494 - [c101]Vishnu Suresh Lokhande, Songwong Tasneeyapant, Abhay Venkatesh, Sathya N. Ravi, Vikas Singh:
Generating Accurate Pseudo-Labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations. CVPR 2020: 11432-11440 - [c100]Vishnu Suresh Lokhande, Aditya Kumar Akash, Sathya N. Ravi, Vikas Singh:
FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret. ECCV (12) 2020: 365-381 - [c99]Wei Hao, Nicholas M. Vogt, Zihang Meng, Seong Jae Hwang, Rebecca L. Koscik, Sterling C. Johnson, Barbara B. Bendlin, Vikas Singh:
Learning Amyloid Pathology Progression from Longitudinal PIB-PET Images in Preclinical Alzheimer's Disease. ISBI 2020: 572-576 - [i30]Vishnu Suresh Lokhande, Aditya Kumar Akash, Sathya N. Ravi, Vikas Singh:
FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret. CoRR abs/2004.01355 (2020) - [i29]Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen:
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators. CoRR abs/2004.14525 (2020) - [i28]Zihang Meng, Sathya N. Ravi, Vikas Singh:
Physarum Powered Differentiable Linear Programming Layers and Applications. CoRR abs/2004.14539 (2020) - [i27]Nishchal K. Verma, Teena Sharma, Sonal Dixit, Pooja Agrawal, Sourya Sengupta, Vikas Singh:
BIDEAL: A Toolbox for Bicluster Analysis - Generation, Visualization and Validation. CoRR abs/2007.13737 (2020) - [i26]Vikas Singh, Pooja Agrawal, Teena Sharma, Nishchal K. Verma:
Improved Adaptive Type-2 Fuzzy Filter with Exclusively Two Fuzzy Membership Function for Filtering Salt and Pepper Noise. CoRR abs/2008.04114 (2020) - [i25]Won Hwa Kim, Mona Jalal, Seong Jae Hwang, Sterling C. Johnson, Vikas Singh:
Online Graph Completion: Multivariate Signal Recovery in Computer Vision. CoRR abs/2008.05060 (2020) - [i24]Vikas Singh, Homanga Bharadhwaj, Nishchal K. Verma:
A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification. CoRR abs/2009.00822 (2020) - [i23]Xingjian Zhen, Rudrasis Chakraborty, Liu Yang, Vikas Singh:
Flow-based Generative Models for Learning Manifold to Manifold Mappings. CoRR abs/2012.10013 (2020)
2010 – 2019
- 2019
- [j25]Seong Jae Hwang, Nagesh Adluru, Won Hwa Kim, Sterling C. Johnson, Barbara B. Bendlin, Vikas Singh:
Associations Between Positron Emission Tomography Amyloid Pathology and Diffusion Tensor Imaging Brain Connectivity in Pre-Clinical Alzheimer's Disease. Brain Connect. 9(2): 162-173 (2019) - [c98]Sathya N. Ravi, Tuan Dinh, Vishnu Suresh Lokhande, Vikas Singh:
Explicitly Imposing Constraints in Deep Networks via Conditional Gradients Gives Improved Generalization and Faster Convergence. AAAI 2019: 4772-4779 - [c97]Yunyang Xiong, Hyunwoo J. Kim, Vikas Singh:
Mixed Effects Neural Networks (MeNets) With Applications to Gaze Estimation. CVPR 2019: 7743-7752 - [c96]Teena Sharma, Vikas Singh, Siddharth Sudhakaran, Nishchal K. Verma:
Fuzzy based Pooling in Convolutional Neural Network for Image Classification. FUZZ-IEEE 2019: 1-6 - [c95]Vikas Singh, Teena Sharma, Nishchal K. Verma, Yan Cui:
Feature Ranking using Robust Fuzzy Score Function for Gene Expression Data. FUZZ-IEEE 2019: 1-6 - [c94]Yunyang Xiong, Ronak Mehta, Vikas Singh:
Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help? ICCV 2019: 1901-1910 - [c93]Yiyou Sun, Sathya N. Ravi, Vikas Singh:
Adaptive Activation Thresholding: Dynamic Routing Type Behavior for Interpretability in Convolutional Neural Networks. ICCV 2019: 4937-4946 - [c92]Ronak Mehta, Rudrasis Chakraborty, Vikas Singh, Yunyang Xiong:
Scaling Recurrent Models via Orthogonal Approximations in Tensor Trains. ICCV 2019: 10570-10578 - [c91]Haoliang Sun, Ronak Mehta, Hao Henry Zhou, Zhichun Huang, Sterling C. Johnson, Vivek Prabhakaran, Vikas Singh:
DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer. ICCV 2019: 10610-10619 - [c90]Rudrasis Chakraborty, Xingjian Zhen, Nicholas Vogt, Barbara B. Bendlin, Vikas Singh:
Dilated Convolutional Neural Networks for Sequential Manifold-Valued Data. ICCV 2019: 10620-10630 - [c89]Seong Jae Hwang, Zirui Tao, Vikas Singh, Won Hwa Kim:
Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples With Applications to Neuroimaging. ICCV 2019: 10691-10700 - [c88]Yunyang Xiong, Hyunwoo J. Kim, Bhargav Tangirala, Ronak Mehta, Sterling C. Johnson, Vikas Singh:
On Training Deep 3D CNN Models with Dependent Samples in Neuroimaging. IPMI 2019: 99-111 - [c87]Seong Jae Hwang, Ronak Mehta, Hyunwoo J. Kim, Sterling C. Johnson, Vikas Singh:
Sampling-free Uncertainty Estimation in Gated Recurrent Units with Applications to Normative Modeling in Neuroimaging. UAI 2019: 809-819 - [i22]Yunyang Xiong, Ronak Mehta, Vikas Singh:
Resource Constrained Neural Network Architecture Search. CoRR abs/1904.03786 (2019) - [i21]Owen Levin, Zihang Meng, Vikas Singh, Xiaojin Zhu:
Fooling Computer Vision into Inferring the Wrong Body Mass Index. CoRR abs/1905.06916 (2019) - [i20]Haoliang Sun, Ronak Mehta, Hao Henry Zhou, Zhichun Huang, Sterling C. Johnson, Vivek Prabhakaran, Vikas Singh:
DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer. CoRR abs/1908.08074 (2019) - [i19]Vishnu Suresh Lokhande, Sathya N. Ravi, Songwong Tasneeyapant, Abhay Venkatesh, Vikas Singh:
Generating Accurate Pseudo-labels via Hermite Polynomials for SSL Confidently. CoRR abs/1909.05479 (2019) - [i18]Sathya N. Ravi, Abhay Venkatesh, Glenn Moo Fung, Vikas Singh:
Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization offers Significant Performance and Efficiency Gains. CoRR abs/1909.12398 (2019) - [i17]Xingjian Zhen, Rudrasis Chakraborty, Nicholas Vogt, Barbara B. Bendlin, Vikas Singh:
Dilated Convolutional Neural Networks for Sequential Manifold-valued Data. CoRR abs/1910.02206 (2019) - [i16]Vikas Singh, Nishchal K. Verma:
An Entropy-based Variable Feature Weighted Fuzzy k-Means Algorithm for High Dimensional Data. CoRR abs/1912.11209 (2019) - [i15]Vikas Singh, Nishchal K. Verma:
mRMR-DNN with Transfer Learning for IntelligentFault Diagnosis of Rotating Machines. CoRR abs/1912.11235 (2019) - 2018
- [j24]Vikas Singh, Raghav Dev, Narendra Kumar Dhar, Pooja Agrawal, Nishchal K. Verma:
Adaptive Type-2 Fuzzy Approach for Filtering Salt and Pepper Noise in Grayscale Images. IEEE Trans. Fuzzy Syst. 26(5): 3170-3176 (2018) - [c86]Seong Jae Hwang, Sathya N. Ravi, Zirui Tao, Hyunwoo J. Kim, Maxwell D. Collins, Vikas Singh:
Tensorize, Factorize and Regularize: Robust Visual Relationship Learning. CVPR 2018: 1014-1023 - [c85]Lopamudra Mukherjee, Sathya N. Ravi, Jiming Peng, Vikas Singh:
A Biresolution Spectral Framework for Product Quantization. CVPR 2018: 3329-3338 - [c84]Zihang Meng, Nagesh Adluru, Hyunwoo J. Kim, Glenn Fung, Vikas Singh:
Efficient Relative Attribute Learning Using Graph Neural Networks. ECCV (14) 2018: 575-590 - [c83]Vikas Singh, Harsh Vardhan, Nishchal K. Verma, Yan Cui:
Optimal Feature Selection using Fuzzy Combination of Feature Subset for Transcriptome Data. FUZZ-IEEE 2018: 1-8 - [c82]Seetaram Maurya, Vikas Singh, Sonal Dixit, Nishchal K. Verma, Al Salour, Jie Liu:
Fusion of Low-level Features with Stacked Autoencoder for Condition based Monitoring of Machines. ICPHM 2018: 1-8 - [c81]Gaurav Saraswat, Vikas Singh, Nishchal K. Verma, Al Salour, Jie Liu:
Prognosis of Diesel Engine (MBT) Using Feature Extraction Techniques: A Comparative Study. ICPHM 2018: 1-6 - [c80]Rogers Jeffrey Leo John, Jignesh M. Patel, Andrew L. Alexander, Vikas Singh, Nagesh Adluru:
A Natural Language Interface for Dissemination of Reproducible Biomedical Data Science. MICCAI (4) 2018: 197-205 - [c79]Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen, Monami Banerjee, Derek Archer, David E. Vaillancourt, Vikas Singh, Baba C. Vemuri:
A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices. NeurIPS 2018: 8897-8908 - [i14]Sathya N. Ravi, Tuan Dinh, Vishnu Sai Rao Lokhande, Vikas Singh:
Constrained Deep Learning using Conditional Gradient and Applications in Computer Vision. CoRR abs/1803.06453 (2018) - [i13]Sathya N. Ravi, Ronak Mehta, Vikas Singh:
Robust Blind Deconvolution via Mirror Descent. CoRR abs/1803.08137 (2018) - [i12]Seong Jae Hwang, Ronak Mehta, Vikas Singh:
Sampling-free Uncertainty Estimation in Gated Recurrent Units with Exponential Families. CoRR abs/1804.07351 (2018) - [i11]Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen, Monami Banerjee, Derek Archer, David E. Vaillancourt, Vikas Singh, Baba C. Vemuri:
Statistical Recurrent Models on Manifold valued Data. CoRR abs/1805.11204 (2018) - [i10]Hao Henry Zhou, Yunyang Xiong, Vikas Singh:
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty. CoRR abs/1806.03563 (2018) - [i9]Seong Jae Hwang, Zirui Tao, Won Hwa Kim, Vikas Singh:
Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging. CoRR abs/1811.09897 (2018) - 2017
- [j23]Felipe Gutierrez-Barragan, Vamsi K. Ithapu, Chris Hinrichs, Camille Maumet, Sterling C. Johnson, Thomas E. Nichols, Vikas Singh:
Accelerating permutation testing in voxel-wise analysis through subspace tracking: A new plugin for SnPM. NeuroImage 159: 79-98 (2017) - [c78]Vamsi K. Ithapu, Risi Kondor, Sterling C. Johnson, Vikas Singh:
The Incremental Multiresolution Matrix Factorization Algorithm. CVPR 2017: 692-701 - [c77]Ligang Zheng, Hyunwoo J. Kim, Nagesh Adluru, Michael A. Newton, Vikas Singh:
Riemannian Variance Filtering: An Independent Filtering Scheme for Statistical Tests on Manifold-Valued Data. CVPR Workshops 2017: 699-708 - [c76]Sathya N. Ravi, Yunyang Xiong, Lopamudra Mukherjee, Vikas Singh:
Filter Flow Made Practical: Massively Parallel and Lock-Free. CVPR 2017: 5009-5018 - [c75]Won Hwa Kim, Mona Jalal, Seong Jae Hwang, Sterling C. Johnson, Vikas Singh:
Online Graph Completion: Multivariate Signal Recovery in Computer Vision. CVPR 2017: 5019-5027 - [c74]Hyunwoo J. Kim, Nagesh Adluru, Heemanshu Suri, Baba C. Vemuri, Sterling C. Johnson, Vikas Singh:
Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging. CVPR 2017: 5777-5786 - [c73]Rudrasis Chakraborty, Vikas Singh, Nagesh Adluru, Baba C. Vemuri:
A Geometric Framework for Statistical Analysis of Trajectories with Distinct Temporal Spans. ICCV 2017: 172-181 - [c72]Hao Henry Zhou, Yilin Zhang, Vamsi K. Ithapu, Sterling C. Johnson, Grace Wahba, Vikas Singh:
When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, $\ell_2$-consistency and Neuroscience Applications. ICML 2017: 4170-4179 - [c71]Gregory Plumb, Lindsay Clark, Sterling C. Johnson, Vikas Singh:
Modeling Cognitive Trends in Preclinical Alzheimer's Disease (AD) via Distributions over Permutations. MICCAI (3) 2017: 683-691 - [p1]Vamsi K. Ithapu, Vikas Singh, Sterling C. Johnson:
Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer's Disease. Deep Learning for Medical Image Analysis 2017: 341-378 - [i8]Vamsi K. Ithapu, Sathya N. Ravi, Vikas Singh:
On architectural choices in deep learning: From network structure to gradient convergence and parameter estimation. CoRR abs/1702.08670 (2017) - [i7]Felipe Gutierrez-Barragan, Vamsi K. Ithapu, Chris Hinrichs, Camille Maumet, Sterling C. Johnson, Thomas E. Nichols, Vikas Singh:
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM. CoRR abs/1703.01506 (2017) - [i6]Vamsi K. Ithapu, Risi Kondor, Sterling C. Johnson, Vikas Singh:
The Incremental Multiresolution Matrix Factorization Algorithm. CoRR abs/1705.05804 (2017) - [i5]Sathya N. Ravi, Maxwell D. Collins, Vikas Singh:
A Deterministic Nonsmooth Frank Wolfe Algorithm with Coreset Guarantees. CoRR abs/1708.06714 (2017) - [i4]Ronak Mehta, Hyunwoo J. Kim, Shulei Wang, Sterling C. Johnson, Ming Yuan, Vikas Singh:
Finding Differentially Covarying Needles in a Temporally Evolving Haystack: A Scan Statistics Perspective. CoRR abs/1711.07575 (2017) - 2016
- [j22]Margam Madhusudhan, Vikas Singh:
Integrated library management systems: Comparative analysis of Koha, Libsys, NewGenLib, and Virtua. Electron. Libr. 34(2): 223-249 (2016) - [c70]Vamsi K. Ithapu, Sathya N. Ravi, Vikas Singh:
On the interplay of network structure and gradient convergence in deep learning. Allerton 2016: 488-495 - [c69]Vikas Singh, Nikhil Baranwal, Rahul Kumar Sevakula, Nishchal K. Verma, Yan Cui:
Layerwise feature selection in Stacked Sparse Auto-Encoder for tumor type prediction. BIBM 2016: 1542-1548 - [c68]Won Hwa Kim, Hyunwoo J. Kim, Nagesh Adluru, Vikas Singh:
Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project (HCP). CVPR 2016: 2443-2451 - [c67]Seong Jae Hwang, Nagesh Adluru, Maxwell D. Collins, Sathya N. Ravi, Barbara B. Bendlin, Sterling C. Johnson, Vikas Singh:
Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks. CVPR 2016: 2517-2525 - [c66]Won Hwa Kim, Seong Jae Hwang, Nagesh Adluru, Sterling C. Johnson, Vikas Singh:
Adaptive Signal Recovery on Graphs via Harmonic Analysis for Experimental Design in Neuroimaging. ECCV (6) 2016: 188-205 - [c65]Lopamudra Mukherjee, Jiming Peng, Trevor Sigmund, Vikas Singh:
Network Flow Formulations for Learning Binary Hashing. ECCV (5) 2016: 366-381 - [c64]Hyunwoo J. Kim, Brandon M. Smith, Nagesh Adluru, Charles R. Dyer, Sterling C. Johnson, Vikas Singh:
Abundant Inverse Regression Using Sufficient Reduction and Its Applications. ECCV (3) 2016: 570-584 - [c63]Vikas Singh, Rahul K. Gupta, Rahul Kumar Sevakula, Nishchal K. Verma:
Comparative analysis of Gaussian mixture model, logistic regression and random forest for big data classification using map reduce. ICIIS 2016: 333-338 - [c62]Sathya N. Ravi, Vamsi K. Ithapu, Sterling C. Johnson, Vikas Singh:
Experimental Design on a Budget for Sparse Linear Models and Applications. ICML 2016: 583-592 - [c61]Matt Straayer, Jim Bales, Dwight Birdsall, Denis C. Daly, Phillip Elliott, Bill Foley, Roy Mason, Vikas Singh, Xuejin Wang:
27.5 A 4GS/s time-interleaved RF ADC in 65nm CMOS with 4GHz input bandwidth. ISSCC 2016: 464-465 - [c60]Hao Henry Zhou, Vamsi K. Ithapu, Sathya Narayanan Ravi, Vikas Singh, Grace Wahba, Sterling C. Johnson:
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease. NIPS 2016: 2496-2504 - [c59]Drew Davidson, Hao Wu, Robert Jellinek, Vikas Singh, Thomas Ristenpart:
Controlling UAVs with Sensor Input Spoofing Attacks. WOOT 2016 - 2015
- [j21]Gregory Plumb, Deepti Pachauri, Risi Kondor, Vikas Singh:
SnFFT: a Julia toolkit for Fourier analysis of functions over permutations. J. Mach. Learn. Res. 16: 3469-3473 (2015) - [j20]Won Hwa Kim, Nagesh Adluru, Moo K. Chung, Ozioma C. Okonkwo, Sterling C. Johnson, Barbara B. Bendlin, Vikas Singh:
Multi-resolution statistical analysis of brain connectivity graphs in preclinical Alzheimer's disease. NeuroImage 118: 103-117 (2015) - [c58]Jia Xu, Lopamudra Mukherjee, Yin Li, Jamieson Warner, James M. Rehg, Vikas Singh:
Gaze-enabled egocentric video summarization via constrained submodular maximization. CVPR 2015: 2235-2244 - [c57]Won Hwa Kim, Barbara B. Bendlin, Moo K. Chung, Sterling C. Johnson, Vikas Singh:
Statistical inference models for image datasets with systematic variations. CVPR 2015: 4795-4803 - [c56]Won Hwa Kim, Sathya N. Ravi, Sterling C. Johnson, Ozioma C. Okonkwo, Vikas Singh:
On Statistical Analysis of Neuroimages with Imperfect Registration. ICCV 2015: 666-674 - [c55]Seong Jae Hwang, Maxwell D. Collins, Sathya N. Ravi, Vamsi K. Ithapu, Nagesh Adluru, Sterling C. Johnson, Vikas Singh:
A Projection Free Method for Generalized Eigenvalue Problem with a Nonsmooth Regularizer. ICCV 2015: 1841-1849 - [c54]Hyunwoo J. Kim, Nagesh Adluru, Monami Banerjee, Baba C. Vemuri, Vikas Singh:
Interpolation on the Manifold of K Component GMMs. ICCV 2015: 2884-2892 - [c53]Lopamudra Mukherjee, Sathya N. Ravi, Vamsi K. Ithapu, Tyler Holmes, Vikas Singh:
An NMF Perspective on Binary Hashing. ICCV 2015: 4184-4192 - [c52]