Zobrazeno 1 - 10
of 30 898
pro vyhledávání: '"Schäffer, A A"'
Linear regression is often deemed inherently interpretable; however, challenges arise for high-dimensional data. We focus on further understanding how linear regression approximates nonlinear responses from high-dimensional functional data, motivated
Externí odkaz:
http://arxiv.org/abs/2411.12060
Retrograde analysis is used in game-playing programs to solve states at the end of a game, working backwards toward the start of the game. The algorithm iterates through and computes the perfect-play value for as many states as resources allow. We in
Externí odkaz:
http://arxiv.org/abs/2411.09089
We propose a novel fine-tuning method to achieve multi-operator learning through training a distributed neural operator with diverse function data and then zero-shot fine-tuning the neural network using physics-informed losses for downstream tasks. O
Externí odkaz:
http://arxiv.org/abs/2411.07239
Autor:
Nateghi, Ramin, Zhou, Ruoji, Saft, Madeline, Schnauss, Marina, Neill, Clayton, Alam, Ridwan, Handa, Nicole, Huang, Mitchell, Li, Eric V, Goldstein, Jeffery A, Schaeffer, Edward M, Nadim, Menatalla, Pourakpour, Fattaneh, Isaila, Bogdan, Felicelli, Christopher, Mehta, Vikas, Nezami, Behtash G, Ross, Ashley, Yang, Ximing, Cooper, Lee AD
Artificial intelligence may assist healthcare systems in meeting increasing demand for pathology services while maintaining diagnostic quality and reducing turnaround time and costs. We aimed to investigate the performance of an institutionally devel
Externí odkaz:
http://arxiv.org/abs/2410.23642
Autor:
Malle, Gunter, Fry, A. A. Schaeffer
The Eaton--Moret\'o conjecture extends the recently-proven Brauer height zero conjecture to blocks with non-abelian defect group, positing equality between the minimal positive heights of a block of a finite group and its defect group. Here we provid
Externí odkaz:
http://arxiv.org/abs/2410.22745
We present an innovative optical imaging system for measuring parameters of a small particle such as a macromolecule or nanoparticle at the quantum limit of sensitivity. In comparison to the conventional confocal interferometric scattering (iSCAT) ap
Externí odkaz:
http://arxiv.org/abs/2410.19417
Autor:
Obbad, Elyas, Mlauzi, Iddah, Miranda, Brando, Schaeffer, Rylan, Obbad, Kamal, Bedi, Suhana, Koyejo, Sanmi
Data selection is crucial for optimizing language model (LM) performance on specific tasks, yet most existing methods fail to effectively consider the target task distribution. Current approaches either ignore task-specific requirements entirely or r
Externí odkaz:
http://arxiv.org/abs/2410.18194
Autor:
Kazdan, Joshua, Schaeffer, Rylan, Dey, Apratim, Gerstgrasser, Matthias, Rafailov, Rafael, Donoho, David L., Koyejo, Sanmi
The increasing presence of AI-generated content on the internet raises a critical question: What happens when generative machine learning models are pretrained on web-scale datasets containing data created by earlier models? Some authors prophesy $\t
Externí odkaz:
http://arxiv.org/abs/2410.16713
Autor:
Rhyu, Jinwook, Schaeffer, Joachim, Li, Michael L., Cui, Xiao, Chueh, William C., Bazant, Martin Z., Braatz, Richard D.
Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid electrolyte interphase formation and the long testing time (~100 days) for cells to reach the end of life. We propos
Externí odkaz:
http://arxiv.org/abs/2410.07458
Autor:
Hirt, Sebastian, Höhl, Andreas, Pohlodek, Johannes, Schaeffer, Joachim, Pfefferkorn, Maik, Braatz, Richard D., Findeisen, Rolf
Model predictive control (MPC) is a powerful tool for controlling complex nonlinear systems under constraints, but often struggles with model uncertainties and the design of suitable cost functions. To address these challenges, we discuss an approach
Externí odkaz:
http://arxiv.org/abs/2410.04982