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pro vyhledávání: '"Thaler OF"'
The Energy Mover's Distance (EMD) has seen use in collider physics as a metric between events and as a geometric method of defining infrared and collinear safe observables. Recently, the Spectral Energy Mover's Distance (SEMD) has been proposed as a
Externí odkaz:
http://arxiv.org/abs/2410.05379
Autor:
Köksal, Abdullatif, Thaler, Marion, Imani, Ayyoob, Üstün, Ahmet, Korhonen, Anna, Schütze, Hinrich
Instruction tuning enhances large language models (LLMs) by aligning them with human preferences across diverse tasks. Traditional approaches to create instruction tuning datasets face serious challenges for low-resource languages due to their depend
Externí odkaz:
http://arxiv.org/abs/2409.12958
Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases and consequently, a major cause for mortality and morbidity worldwide. Accurate assessment of myocardial tissue viability for post-MI patients is critical for diagnosis a
Externí odkaz:
http://arxiv.org/abs/2409.12792
Neural Networks (NNs) are promising models for refining the accuracy of molecular dynamics, potentially opening up new fields of application. Typically trained bottom-up, atomistic NN potential models can reach first-principle accuracy, while coarse-
Externí odkaz:
http://arxiv.org/abs/2408.15852
Autor:
Asai, Shoji, Ballarino, Amalia, Bose, Tulika, Cranmer, Kyle, Cyr-Racine, Francis-Yan, Demers, Sarah, Geddes, Cameron, Gershtein, Yuri, Heeger, Karsten, Heinemann, Beate, Hewett, JoAnne, Huber, Patrick, Mahn, Kendall, Mandelbaum, Rachel, Maricic, Jelena, Merkel, Petra, Monahan, Christopher, Murayama, Hitoshi, Onyisi, Peter, Palmer, Mark, Raubenheimer, Tor, Sanchez, Mayly, Schnee, Richard, Seidel, Sally, Seo, Seon-Hee, Thaler, Jesse, Touramanis, Christos, Vieregg, Abigail, Weinstein, Amanda, Winslow, Lindley, Yu, Tien-Tien, Zwaska, Robert
This is the report from the 2023 Particle Physics Project Prioritization Panel (P5) approved by High Energy Physics Advisory Panel (HEPAP) on December 8, 2023. The final version was made public on May 8, 2024 and submitted to DOE SC and NSF MPS.
Externí odkaz:
http://arxiv.org/abs/2407.19176
Deconvolving ("unfolding'') detector distortions is a critical step in the comparison of cross section measurements with theoretical predictions in particle and nuclear physics. However, most existing approaches require histogram binning while many t
Externí odkaz:
http://arxiv.org/abs/2407.11284
Autor:
Wang, Ze, Wu, Zekun, Guan, Xin, Thaler, Michael, Koshiyama, Adriano, Lu, Skylar, Beepath, Sachin, Ertekin Jr., Ediz, Perez-Ortiz, Maria
The use of Large Language Models (LLMs) in hiring has led to legislative actions to protect vulnerable demographic groups. This paper presents a novel framework for benchmarking hierarchical gender hiring bias in Large Language Models (LLMs) for resu
Externí odkaz:
http://arxiv.org/abs/2406.15484
Extracting scientific understanding from particle-physics experiments requires solving diverse learning problems with high precision and good data efficiency. We propose the Lorentz Geometric Algebra Transformer (L-GATr), a new multi-purpose architec
Externí odkaz:
http://arxiv.org/abs/2405.14806
Autor:
Arnold, Robert, Prassl, Anton J., Neic, Aurel, Thaler, Franz, Augustin, Christoph M., Gsell, Matthias A. F., Gillette, Karli, Manninger, Martin, Scherr, Daniel, Plank, Gernot
Background and Objective: Data from electro-anatomical mapping (EAM) systems are playing an increasingly important role in computational modeling studies for the patient-specific calibration of digital twin models. However, data exported from commerc
Externí odkaz:
http://arxiv.org/abs/2403.10394
Many machine learning applications involve learning a latent representation of data, which is often high-dimensional and difficult to directly interpret. In this work, we propose "Moment Pooling", a natural extension of Deep Sets networks which drast
Externí odkaz:
http://arxiv.org/abs/2403.08854