71 citations · 100 across the 10 of their papers we have counts for
10 papers
Self-Supervised Contextual Bandits in Computer Vision
Aniket Anand Deshmukh, Abhimanu Kumar, Levi Boyles +3
Contextual bandits are a common problem faced by machine learning practitioners in domains as diverse as hypothesis testing to product recommendations. There have been a lot of app…
Data Transformation Insights in Self-supervision with Clustering Tasks
Abhimanu Kumar, Aniket Anand Deshmukh, Urun Dogan +2
Self-supervision is key to extending use of deep learning for label scarce domains. For most of self-supervised approaches data transformations play an important role. However, up…
Distributed Training of Deep Neural Networks with Theoretical Analysis: Under SSP Setting
Abhimanu Kumar, Pengtao Xie, Junming Yin +1
We propose a distributed approach to train deep neural networks (DNNs), which has guaranteed convergence theoretically and great scalability empirically: close to 6 times faster on…
Scalable Modeling of Conversational-role based Self-presentation Characteristics in Large Online Forums
Abhimanu Kumar, Shriphani Palakodety, Chong Wang +3
Online discussion forums are complex webs of overlapping subcommunities (macrolevel structure, across threads) in which users enact different roles depending on which subcommunity…
Distributed Machine Learning via Sufficient Factor Broadcasting
Pengtao Xie, Jin Kyu Kim, Yi Zhou +4
Matrix-parametrized models, including multiclass logistic regression and sparse coding, are used in machine learning (ML) applications ranging from computer vision to computational…
High-Performance Distributed ML at Scale through Parameter Server Consistency Models
Wei Dai, Abhimanu Kumar, Jinliang Wei +3
As Machine Learning (ML) applications increase in data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory deman…