Papers

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machine learning Oct 08 Your Prompt Should Do More: Effects of Retrieval Instructions in Embedding Models by Amanda Myntti, Jenna Kanerva, Veronika Laippala, Filip Ginter machine learning Oct 07 Conformal Prediction Sets Quantify Information Gain: A Theoretical Perspective by Kevin Zhang, Stephen Bates machine learning Oct 06 Base Models Can Reason By Taking a Cue From Training Data by Sophie L. Wang, Amil Dravid, Rulin Shao, Kevin Farhat, Sewon Min, Alexei A. Efros machine learning Oct 06 Learning to Read the Contextual Tokens in Diffusion Transformers by Omer Dahary, Etai Sella, Hadar Averbuch-Elor, Daniel Cohen-Or, Or Patashnik machine learning Oct 06 Deep Learning for Sleep Heart Rate Estimation from Accelerometers: Toward Population-Scale Cardiac Insight Without Optical Sensors by Tanbin Islam Rohan, Pranjol Sen Gupta, Tanusree Debi, Nazmus Sakib machine learning Oct 06 Private online learning and prediction for Littlestone classes by Amartya Sanyal machine learning Oct 05 What Should World Models Forget? Stratified Retention for Continual Adaptation by Nishit Anand, Ramani Duraiswami, Dinesh Manocha machine learning Oct 05 RNADyn: A Benchmark for Generating and Understanding RNA Dynamics by Yiming Huang, Lennart Bastian, Hanqun Cao, Luis Vollmers, Tolga Birdal machine learning Oct 05 LESSER: Post-Training Data Selection with Output-Layer Gradients by Lyuxin David Zhang, Eric Wong, Surbhi Goel, Anton Xue machine learning Oct 05 Decoding the Functional Roles of Register and High-Norm Patch Tokens in Vision Transformers by Neel Varma, Andrew Rufail, Dipika Khullar, Vasu Sharma