3 citations · 4 across the 2 of their papers we have counts for
3 papers
cs.LG2023★ 3 cited
Early Weight Averaging meets High Learning Rates for LLM Pre-training
Sunny Sanyal, Atula Neerkaje, Jean Kaddour +2
Training Large Language Models (LLMs) incurs significant cost; hence, any strategy that accelerates model convergence is helpful. In this paper, we investigate the ability of a sim…
cs.CV2023★ 1 cited
Rewarded meta-pruning: Meta Learning with Rewards for Channel Pruning
Athul Shibu, Abhishek Kumar, Heechul Jung +1
Convolutional Neural Networks (CNNs) have a large number of parameters and take significantly large hardware resources to compute, so edge devices struggle to run high-level networ…
eess.SP2020
Adaptive Contention Window Design using Deep Q-learning
Abhishek Kumar, Gunjan Verma, Chirag Rao +2
We study the problem of adaptive contention window (CW) design for random-access wireless networks. More precisely, our goal is to design an intelligent node that can dynamically a…