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ORCIDSathya N. Ravi
Person information
- affiliation: University of Illinois at Chicago, IL, USA
- affiliation (former): University of Wisconsin - Madison, Department of Industrial and Systems Engineering
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2020 – today
- 2024
[c29]Hamidreza Almasi, Harsh Mishra, Balajee Vamanan
, Sathya N. Ravi:
Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization. ICLR 2024- [c28]Gyeongeun Lee, Zhu Wang, Sathya N. Ravi, Natalie Parde:
EmpatheticFIG at WASSA 2024 Empathy and Personality Shared Task: Predicting Empathy and Emotion in Conversations with Figurative Language. WASSA 2024: 441-447 - [i21]Homaira Huda Shomee, Zhu Wang, Sathya N. Ravi, Sourav Medya:
A Comprehensive Survey on AI-based Methods for Patents. CoRR abs/2404.08668 (2024) - 2023
- [c27]Ronak Mehta, Sathya N. Ravi, Vikas Singh:
Robustness and Convergence of Mirror Descent for Blind Deconvolution. ICASSP 2023: 1-5 - [c26]Sourav Pal, Zhanpeng Zeng, Sathya N. Ravi, Vikas Singh:
Controlled Differential Equations on Long Sequences via Non-standard Wavelets. ICML 2023: 26820-26836 - [c25]Zhu Wang, Sourav Medya, Sathya N. Ravi:
Implicit Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis. NeurIPS 2023 - [i20]Harsh Mishra, Jurijs Nazarovs, Manmohan Dogra, Sathya N. Ravi:
Using Intermediate Forward Iterates for Intermediate Generator Optimization. CoRR abs/2302.02336 (2023) - [i19]Zhu Wang, Sourav Medya, Sathya N. Ravi:
Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis. CoRR abs/2302.05608 (2023) - [i18]Hamidreza Almasi, Harsh Mishra, Balajee Vamanan, Sathya N. Ravi:
Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization. CoRR abs/2302.05865 (2023) - [i17]Zhu Wang, Praveen Raj Veluswami, Harsh Mishra, Sathya N. Ravi:
Accelerated Neural Network Training with Rooted Logistic Objectives. CoRR abs/2310.03890 (2023) - 2022
- [j2]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) - [c24]Ronak Mehta, Sourav Pal, Vikas Singh, Sathya N. Ravi:
Deep Unlearning via Randomized Conditionally Independent Hessians. CVPR 2022: 10412-10421 - [c23]Vishnu Suresh Lokhande, Rudrasis Chakraborty, Sathya N. Ravi, Vikas Singh:
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets. CVPR 2022: 10422-10431 - [i16]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) - [i15]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) - [i14]Ronak Mehta, Sourav Pal, Vikas Singh, Sathya N. Ravi:
Deep Unlearning via Randomized Conditionally Independent Hessians. CoRR abs/2204.07655 (2022) - 2021
- [c22]Aditya Kumar Akash, Vishnu Suresh Lokhande, Sathya N. Ravi, Vikas Singh:
Learning Invariant Representations using Inverse Contrastive Loss. AAAI 2021: 6582-6591 - [c21]Zihang Meng, Sathya N. Ravi, Vikas Singh:
Physarum Powered Differentiable Linear Programming Layers and Applications. AAAI 2021: 8939-8949 - [c20]Zihang Meng, Vikas Singh, Sathya N. Ravi:
Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators. ICCV 2021: 11615-11624 - [c19]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 - [c18]Zihang Meng, Lopamudra Mukherjee, Yichao Wu, Vikas Singh, Sathya N. Ravi:
Differentiable Optimization of Generalized Nondecomposable Functions using Linear Programs. NeurIPS 2021: 29129-29141 - [c17]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 - [i13]Aditya Kumar Akash, Vishnu Suresh Lokhande, Sathya N. Ravi, Vikas Singh:
Learning Invariant Representations using Inverse Contrastive Loss. CoRR abs/2102.08343 (2021) - [i12]Zihang Meng, Vikas Singh, Sathya N. Ravi:
Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators. CoRR abs/2108.08891 (2021) - [i11]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) - 2020
- [c16]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 - [c15]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 - [c14]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 - [i10]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) - [i9]Zihang Meng, Sathya N. Ravi, Vikas Singh:
Physarum Powered Differentiable Linear Programming Layers and Applications. CoRR abs/2004.14539 (2020)
2010 – 2019
- 2019
- [j1]Sathya N. Ravi, Maxwell D. Collins, Vikas Singh:
A Deterministic Nonsmooth Frank Wolfe Algorithm with Coreset Guarantees. INFORMS J. Optim. 1(2): 120-142 (2019) - [c13]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 - [c12]Sathya N. Ravi:
Numerical Optimization to AI, and Back. AAAI 2019: 9894-9895 - [c11]Yiyou Sun, Sathya N. Ravi, Vikas Singh:
Adaptive Activation Thresholding: Dynamic Routing Type Behavior for Interpretability in Convolutional Neural Networks. ICCV 2019: 4937-4946 - [i8]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) - [i7]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) - 2018
- [c10]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 - [c9]Lopamudra Mukherjee, Sathya N. Ravi, Jiming Peng, Vikas Singh:
A Biresolution Spectral Framework for Product Quantization. CVPR 2018: 3329-3338 - [i6]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) - [i5]Sathya N. Ravi, Ronak Mehta, Vikas Singh:
Robust Blind Deconvolution via Mirror Descent. CoRR abs/1803.08137 (2018) - 2017
- [c8]Sathya N. Ravi, Yunyang Xiong, Lopamudra Mukherjee, Vikas Singh:
Filter Flow Made Practical: Massively Parallel and Lock-Free. CVPR 2017: 5009-5018 - [i4]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) - [i3]Sathya N. Ravi, Maxwell D. Collins, Vikas Singh:
A Deterministic Nonsmooth Frank Wolfe Algorithm with Coreset Guarantees. CoRR abs/1708.06714 (2017) - 2016
- [c7]Vamsi K. Ithapu, Sathya N. Ravi, Vikas Singh:
On the interplay of network structure and gradient convergence in deep learning. Allerton 2016: 488-495 - [c6]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 - [c5]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 - [c4]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 - 2015
- [c3]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 - [c2]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 - [c1]Lopamudra Mukherjee, Sathya N. Ravi, Vamsi K. Ithapu, Tyler Holmes, Vikas Singh:
An NMF Perspective on Binary Hashing. ICCV 2015: 4184-4192 - [i2]Vamsi K. Ithapu, Sathya N. Ravi, Vikas Singh:
Convergence of gradient based pre-training in Denoising autoencoders. CoRR abs/1502.03537 (2015) - [i1]Vamsi K. Ithapu, Sathya N. Ravi, Vikas Singh:
On the interplay of network structure and gradient convergence in deep learning. CoRR abs/1511.05297 (2015)
Coauthor Index
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last updated on 2024-10-08 21:30 CEST by the dblp team
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