Deep Learning
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks
Input reduction reduces the length of the input context before feeding to downstream LLMs.
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models
The in-context learning (ICL) capability of pre-trained models based on the transformer architecture has received growing interest in recent years.
LLaMo: Large Language Model-based Molecular Graph Assistant
Thus, we propose LLaMo: L arge La nguage Model-based Mo lecular graph assistant, which is an end-to-end trained large molecular graph language model.
A Label is Worth a Thousand Images in Dataset Distillation
Understanding how and why data distillation methods work is vital not only for improving these methods but also for revealing fundamental characteristics of "good" training data.
Neglected Hessian component explains mysteries in sharpness regularization
SAM can improve generalization in deep learning. Seemingly similar methods like weight noise and gradient penalties often fail to provide such benefits. We investigate this inconsistency and reveal its connection to the the structure of the Hessian of the loss.
Energy-based Hopfield Boosting for Out-of-Distribution Detection
Hopfield Boosting, a boosting approach, which leverages modern Hopfield energy to sharpen the decision boundary between the in-distribution and OOD data.