Sathya N. Ravi | DeepAI

DeepAI

Featured Co-authors

Vikas Singh
48 publications
Rudrasis Chakraborty
28 publications
Maxwell D. Collins
13 publications
Glenn Fung
11 publications
Vamsi K. Ithapu
11 publications
Ronak Mehta
10 publications
Yunyang Xiong
10 publications
Zihang Meng
9 publications
Vishnu Suresh Lokhande
8 publications
Tuan Dinh
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Jurijs Nazarovs
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∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙