2 citations · 2 across the 4 of their papers we have counts for
4 papers
Memory Efficient and Staleness Free Pipeline Parallel DNN Training Framework with Improved Convergence Speed
Ankita Dutta, Nabendu Chaki, Rajat K. De
High resource requirement for Deep Neural Network (DNN) training across multiple GPUs necessitates development of various parallelism techniques. In this paper, we introduce two in…
TiMePReSt: Time and Memory Efficient Pipeline Parallel DNN Training with Removed Staleness
Ankita Dutta, Nabendu Chaki, Rajat K. De
DNN training is time-consuming and requires efficient multi-accelerator parallelization, where a single training iteration is split over available accelerators. Current approaches…
Bayes-Nash Generative Privacy Against Membership Inference Attacks
Tao Zhang, Rajagopal Venkatesaramani, Rajat K. De +2
Membership inference attacks (MIAs) pose significant privacy risks by determining whether individual data is in a dataset. While differential privacy (DP) mitigates these risks, it…
A Game-Theoretic Approach to Privacy-Utility Tradeoff in Sharing Genomic Summary Statistics
Tao Zhang, Rajagopal Venkatesaramani, Rajat K. De +2
The advent of online genomic data-sharing services has sought to enhance the accessibility of large genomic datasets by allowing queries about genetic variants, such as summary sta…