94 citations · 301 across the 30 of their papers we have counts for
3 papers · 1 filter
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity
Charlie Hou, Kiran Koshy Thekumparampil, Michael Shavlovsky +3
On tabular data, a significant body of literature has shown that current deep learning (DL) models perform at best similarly to Gradient Boosted Decision Trees (GBDTs), while signi…
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…
Understanding Self-Distillation in the Presence of Label Noise
Rudrajit Das, Sujay Sanghavi
Self-distillation (SD) is the process of first training a \enquote{teacher} model and then using its predictions to train a \enquote{student} model with the \textit{same} architect…