Papers

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machine learning Oct 12 Designing Protein Structures from Natural Language by Joseph Lee, Yanjing Li, Bo Qiang, Gregory Lau, Allyson Ettinger, Noah A. Smith, Rory Donovan-Maiye, David Baker, Hanna Hajishirzi, Pang Wei Koh machine learning Oct 12 Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping by Danil Rudenko, Georgii Bulgakov, Nikolay Kartashev, Ivan Rubachev, Daniil Krasylnikov, Artem Babenko machine learning Oct 12 Prediction Is Not a Sufficient Memory State by Robert Jenkinson Alvarez machine learning Oct 12 Partially Informed Stochastic Optimization via Zeroth-Order Residual Tracking by El Mahdi Chayti, Alireza Mirrokni, Martin Jaggi 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 Specialists Learn Faster: Knowledge Organization in Pre-training Shapes Knowledge Update Efficiency by Sheng Zhao, Yuhao Zhu 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