145 citations · 222 across the 14 of their papers we have counts for
16 papers
Offline RL With Resource Constrained Online Deployment
Jayanth Reddy Regatti, Aniket Anand Deshmukh, Frank Cheng +3
Offline reinforcement learning is used to train policies in scenarios where real-time access to the environment is expensive or impossible. As a natural consequence of these harsh…
An efficient reverse-lookup table based strategy for solving the synonym and cache coherence problem in virtually indexed, virtually tagged caches
Madhav P. Desai, Aniket Deshmukh
Virtually indexed and virtually tagged (VIVT) caches are an attractive option for micro-processor level-1 caches, because of their fast response time and because they are cheaper t…
Representation Learning for Clustering via Building Consensus
Aniket Anand Deshmukh, Jayanth Reddy Regatti, Eren Manavoglu +1
In this paper, we focus on unsupervised representation learning for clustering of images. Recent advances in deep clustering and unsupervised representation learning are based on t…
Consensus Clustering With Unsupervised Representation Learning
Jayanth Reddy Regatti, Aniket Anand Deshmukh, Eren Manavoglu +1
Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techni…
Zero Shot Domain Generalization
Udit Maniyar, Joseph K J, Aniket Anand Deshmukh +2
Standard supervised learning setting assumes that training data and test data come from the same distribution (domain). Domain generalization (DG) methods try to learn a model that…
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…