activity
20152025
most citedOn architectural choices in deep learning: From network structure to gradient convergence and parameter estimation

5 citations · 8 across the 8 of their papers we have counts for

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9 papers · 1 filter

cs.LG2023

Using Intermediate Forward Iterates for Intermediate Generator Optimization

Harsh Mishra, Jurijs Nazarovs, Manmohan Dogra +1

Score-based models have recently been introduced as a richer framework to model distributions in high dimensions and are generally more suitable for generative tasks. In score-base…

cs.LG2023

Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization

Hamidreza Almasi, Harsh Mishra, Balajee Vamanan +1

Modern ML applications increasingly rely on complex deep learning models and large datasets. There has been an exponential growth in the amount of computation needed to train the l…

cs.LG2022

Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets

Vishnu Suresh Lokhande, Rudrasis Chakraborty, Sathya N. Ravi +1

Pooling multiple neuroimaging datasets across institutions often enables improvements in statistical power when evaluating associations (e.g., between risk factors and disease outc…

cs.LG20222 cited

Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data

Jurijs Nazarovs, Rudrasis Chakraborty, Songwong Tasneeyapant +2

Panel data involving longitudinal measurements of the same set of participants taken over multiple time points is common in studies to understand childhood development and disease…

cs.LG2021

Learning Invariant Representations using Inverse Contrastive Loss

Aditya Kumar Akash, Vishnu Suresh Lokhande, Sathya N. Ravi +1

Learning invariant representations is a critical first step in a number of machine learning tasks. A common approach corresponds to the so-called information bottleneck principle i…

cs.LG2019

Generating Accurate Pseudo-labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations

Vishnu Suresh Lokhande, Songwong Tasneeyapant, Abhay Venkatesh +2

Rectified Linear Units (ReLUs) are among the most widely used activation function in a broad variety of tasks in vision. Recent theoretical results suggest that despite their excel…