Papers | simpleprog

machine learning Oct 12 Inference-Time Steering Across Scaffolds and Epitopes for Sample-Efficient De Novo Nanobody Design by Mason Minot, Simon Friedensohn, Cédric R. Weber

machine learning Oct 12 Returning to Development States: When Full Sandbox Snapshots Help Coding Agents by Daniel Thi Graviet, Tanmay Shah

machine learning Oct 12 Specialists Learn Faster: Knowledge Organization in Pre-training Shapes Knowledge Update Efficiency by Sheng Zhao, Yuhao Zhu

machine learning Oct 12 On the Design Space of Continuous Diffusion Language Models by Oscar Davis, Daan Roos, Alice Bizeul, Amitis Shidani, Victor Turrisi, Louis Béthune, Pierre Ablin, Marco Cuturi

machine learning Oct 12 Shared Canonical Component Removal Beyond Expert Views by Jose Roberto Tello Ayala, Akl C. Fahed, Roy Perlis, Finale Doshi-Velez

machine learning Oct 12 TabMF: Learning Dataset Representations with Tabular Foundation Models by Rafiq Kamel, Nicolas Atienza, Aziz Fouché, Nicola Cancedda, Josif Grabocka

machine learning Oct 12 Spanning Diffusion and Flow-based Posterior Sampling via Exact Gaussian Conditioning by Teo Deveney, Lea Bogensperger, Hok Shing Wong

machine learning Oct 12 FORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems by Vishnu Arun, Adam Obeng, Tianyou Wang, Zilin Ren, Mukuntha Narayanan Sundararaman, Han Wang, Shivin Thukral, Zhixiang Wang, Saurabh Vishwas Joshi, Darren Reger

machine learning Oct 09 Bi-FORK: Generative Modeling of High-Dimensional Bifurcating Systems by Anna Zimmel, Fleur Hendriks, Markus Holzleitner, Florian Sestak, Martin Weichselbaumer, Vlado Menkovski, Johannes Brandstetter

machine learning Oct 09 One Block, Multiple Depths: Recurrent Vision Transformers with Depth-Programmed Experts by Adrian Bulat, Yassine Ouali, Georgios Tzimiropoulos