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cs.LG2026
Data Distribution as a Lever for Guiding Optimizers Toward Superior Generalization in LLMs
Tushaar Gangavarapu, Jiping Li, Christopher Vattheuer +2
Can modifying the training data distribution guide optimizers toward solutions with improved generalization when training large language models (LLMs)? In this work, we theoretical…
cs.LG2025
An Overview of Low-Rank Structures in the Training and Adaptation of Large Models
Laura Balzano, Tianjiao Ding, Benjamin D. Haeffele +5
The substantial computational demands of modern large-scale deep learning present significant challenges for efficient training and deployment. Recent research has revealed a wides…
cs.LG2024
On How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks
William T. Redman, Zhangyang Wang, Alessandro Ingrosso +1
Since its use in the Lottery Ticket Hypothesis, iterative magnitude pruning (IMP) has become a popular method for extracting sparse subnetworks that can be trained to high performa…