2 citations · 3 across the 3 of their papers we have counts for
7 papers
Data-Driven Low-Rank Neural Network Compression
Dimitris Papadimitriou, Swayambhoo Jain
Despite many modern applications of Deep Neural Networks (DNNs), the large number of parameters in the hidden layers makes them unattractive for deployment on devices with storage…
Efficacy of Bayesian Neural Networks in Active Learning
Vineeth Rakesh, Swayambhoo Jain
Obtaining labeled data for machine learning tasks can be prohibitively expensive. Active learning mitigates this issue by exploring the unlabeled data space and prioritizing the se…
Matrix Completion in the Unit Hypercube via Structured Matrix Factorization
Emanuele Bugliarello, Swayambhoo Jain, Vineeth Rakesh
Several complex tasks that arise in organizations can be simplified by mapping them into a matrix completion problem. In this paper, we address a key challenge faced by our company…
Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks
Fatemeh Sheikholeslami, Swayambhoo Jain, Georgios B. Giannakis
Despite their unprecedented performance in various domains, utilization of Deep Neural Networks (DNNs) in safety-critical environments is severely limited in the presence of even s…
Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing
Mohammadreza Soltani, Swayambhoo Jain, Abhinav Sambasivan
Recently, Generative Adversarial Networks (GANs) have emerged as a popular alternative for modeling complex high dimensional distributions. Most of the existing works implicitly as…
Rank-to-engage: New Listwise Approaches to Maximize Engagement
Swayambhoo Jain, Akshay Soni, Nikolay Laptev +1
For many internet businesses, presenting a given list of items in an order that maximizes a certain metric of interest (e.g., click-through-rate, average engagement time etc.) is c…