TMLR

Transformers trained on proteins can learn to attend to Euclidean distance

Isaac Ellmen, Constantin Schneider, Matthew I. J. Raybould, Charlotte M. Deane

Journal ref Transactions on Machine Learning Research (2025) https://openreview.net/forum?id=mU59bDyqqv

LLaVA-Video: Video Instruction Tuning With Synthetic Data

Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, Chunyuan Li

机构 * S-Lab, Nanyang Technological University（南洋理工大学S实验室） ； BUPT（北京邮电大学） ； ByteDance（字节跳动） 纠错

Comments Project page: https://llava-vl.github.io/blog/2024-09-30-llava-video/; Accepted at TMLR

Loss Landscape Degeneracy and Stagewise Development in Transformers

Jesse Hoogland, George Wang, Matthew Farrugia-Roberts, Liam Carroll, Susan Wei, Daniel Murfet

机构 * Timaeus ； Department of Computer Science, University of Oxford（计算机科学系，牛津大学） ； Department of Econometrics and Business Statistics, Monash University（计量经济学与商业统计系，墨尔本大学） ； School of Mathematics and Statistics, the University of Melbourne（数学与统计学系，墨尔本大学） 纠错

Comments To appear, TMLR. Material on essential dynamics from v1 of this preprint has been removed and developed in arXiv:2501.17745