Many Property
Research on "many property" problems focuses on predicting or explaining multiple properties simultaneously, moving beyond single-property analyses. Current efforts concentrate on developing and improving multimodal deep learning models, such as transformer-based architectures and diffusion models, along with refining explanation methods like Shapley values and investigating the properties of various kernel-based approaches. This research is significant because it addresses the limitations of single-property models and enables more comprehensive understanding and prediction in diverse fields, including materials science, drug discovery, and climate modeling.
Papers
ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models
Ziniu Li, Tian Xu, Yushun Zhang, Zhihang Lin, Yang Yu, Ruoyu Sun, Zhi-Quan Luo
On the Properties and Estimation of Pointwise Mutual Information Profiles
Paweł Czyż, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx