124 citations · 169 across the 7 of their papers we have counts for
7 papers
Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs
George Pu, Anirudh Jain, Jihan Yin +1
As foundation models continue to exponentially scale in size, efficient methods of adaptation become increasingly critical. Parameter-efficient fine-tuning (PEFT), a recent class o…
Confounding Tradeoffs for Neural Network Quantization
Sahaj Garg, Anirudh Jain, Joe Lou +1
Many neural network quantization techniques have been developed to decrease the computational and memory footprint of deep learning. However, these methods are evaluated subject to…
Dynamic Precision Analog Computing for Neural Networks
Sahaj Garg, Joe Lou, Anirudh Jain +1
Analog electronic and optical computing exhibit tremendous advantages over digital computing for accelerating deep learning when operations are executed at low precision. In this w…
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
Vishnu Sarukkai, Anirudh Jain, Burak Uzkent +1
Satellite images hold great promise for continuous environmental monitoring and earth observation. Occlusions cast by clouds, however, can severely limit coverage, making ground in…
VizSeq: A Visual Analysis Toolkit for Text Generation Tasks
Changhan Wang, Anirudh Jain, Danlu Chen +1
Automatic evaluation of text generation tasks (e.g. machine translation, text summarization, image captioning and video description) usually relies heavily on task-specific metrics…
Practical Deep Learning with Bayesian Principles
Kazuki Osawa, Siddharth Swaroop, Anirudh Jain +4
Bayesian methods promise to fix many shortcomings of deep learning, but they are impractical and rarely match the performance of standard methods, let alone improve them. In this p…
Variational Inference as an alternative to MCMC for parameter estimation and model selection
Geetakrishnasai Gunapati, Anirudh Jain, P. K. Srijith +1
Most applications of Bayesian Inference for parameter estimation and model selection in astrophysics involve the use of Monte Carlo techniques such as Markov Chain Monte Carlo (MCM…