arXiv:2607. 22961v1 Announce Type: new Abstract: Verbalized Machine Learning (VML) parameterizes a model as a natural-language prompt that an LLM evaluates as f(x; theta).
By Yan Zhang, Shikan Lian, Shibo Li
arXiv:2601.17609v3 Announce Type: replace
Abstract: In domains like medicine and finance, large-scale labeled data is costly and often unavailable, leading to models trained on small datasets that st...
By Sara Rezaeimanesh, Kundan Thind, Farzan Siddiqui, Mohammad M. Ghassemi
arXiv:2609.39525v1 Announce Type: new
Abstract: Casting Bayesian inference as a neural network optimization problem targeting an amortized posterior is attractive, as it extends to otherwise intracta...
By Hans Olischl\"ager, Svenja Jedhoff, \v{S}imon Kucharsk\'y, Aayush Mishra, Stefan T. Radev, Paul B\"urkner
arXiv:2407. 12288v5 Announce Type: replace-cross Abstract: The progress of machine learning over the past decade is undeniable.
By Hong Jun Jeon, Benjamin Van Roy
arXiv:2609.38764v1 Announce Type: new
Abstract: Language models are typically pretrained from random initialization. Recent work challenges this convention, showing that a brief warm-up on abstract,...
By Zachary Shinnick, Hemanth Saratchandran, Damien Teney, Anton van den Hengel
arXiv:2608. 10867v1 Announce Type: new Abstract: Gradient-free post-training has emerged as a compelling alternative to gradient-based optimization for large language models (LLMs), but existing approaches remain costly.
By Nigel Bastian Cendra, Abdelhamid Ezzerg, Fernando Julio Cendra, Jeremias Knoblauch, Jakob Zeitler