2 citations · 2 across the 4 of their papers we have counts for
4 papers
Understanding the Emergence of Seemingly Useless Features in Next-Token Predictors
Mark Rofin, Jalal Naghiyev, Michael Hahn
Trained Transformers have been shown to compute abstract features that appear redundant for predicting the immediate next token. We identify which components of the gradient signal…
A Machine Learning Approach That Beats Large Rubik's Cubes
Alexander Chervov, Kirill Khoruzhii, Nikita Bukhal +9
The paper proposes a novel machine learning-based approach to the pathfinding problem on extremely large graphs. This method leverages diffusion distance estimation via a neural ne…
Layer by Layer: Uncovering Hidden Representations in Language Models
Oscar Skean, Md Rifat Arefin, Dan Zhao +4
From extracting features to generating text, the outputs of large language models (LLMs) typically rely on the final layers, following the conventional wisdom that earlier layers c…
Predicting Coronary Heart Disease Using a Suite of Machine Learning Models
Jamal Al-Karaki, Philip Ilono, Sanchit Baweja +3
Coronary Heart Disease affects millions of people worldwide and is a well-studied area of healthcare. There are many viable and accurate methods for the diagnosis and prediction of…