Zobrazeno 1 - 10
of 264
pro vyhledávání: '"Hassidim, Avinatan"'
Autor:
Kiraly, Atilla P., Baur, Sebastien, Philbrick, Kenneth, Mahvar, Fereshteh, Yatziv, Liron, Chen, Tiffany, Sterling, Bram, George, Nick, Jamil, Fayaz, Tang, Jing, Bailey, Kai, Ahmed, Faruk, Goel, Akshay, Ward, Abbi, Yang, Lin, Sellergren, Andrew, Matias, Yossi, Hassidim, Avinatan, Shetty, Shravya, Golden, Daniel, Azizi, Shekoofeh, Steiner, David F., Liu, Yun, Thelin, Tim, Pilgrim, Rory, Kirmizibayrak, Can
Robust medical Machine Learning (ML) models have the potential to revolutionize healthcare by accelerating clinical research, improving workflows and outcomes, and producing novel insights or capabilities. Developing such ML models from scratch is co
Externí odkaz:
http://arxiv.org/abs/2411.15128
Two prominent objectives in social choice are utilitarian - maximizing the sum of agents' utilities, and leximin - maximizing the smallest agent's utility, then the second-smallest, etc. Utilitarianism is typically computationally easier to attain bu
Externí odkaz:
http://arxiv.org/abs/2409.10395
Autor:
Cattan, Arie, Jacovi, Alon, Fabrikant, Alex, Herzig, Jonathan, Aharoni, Roee, Rashkin, Hannah, Marcus, Dror, Hassidim, Avinatan, Matias, Yossi, Szpektor, Idan, Caciularu, Avi
Despite recent advancements in Large Language Models (LLMs), their performance on tasks involving long contexts remains sub-optimal. In-Context Learning (ICL) with few-shot examples may be an appealing solution to enhance LLM performance in this scen
Externí odkaz:
http://arxiv.org/abs/2406.13632
Autor:
Shani, Lior, Rosenberg, Aviv, Cassel, Asaf, Lang, Oran, Calandriello, Daniele, Zipori, Avital, Noga, Hila, Keller, Orgad, Piot, Bilal, Szpektor, Idan, Hassidim, Avinatan, Matias, Yossi, Munos, Rémi
Reinforcement Learning from Human Feedback (RLHF) has become the standard approach for aligning Large Language Models (LLMs) with human preferences, allowing LLMs to demonstrate remarkable abilities in various tasks. Existing methods work by emulatin
Externí odkaz:
http://arxiv.org/abs/2405.14655
Autor:
Goldstein, Ariel, Ham, Eric, Schain, Mariano, Nastase, Samuel, Zada, Zaid, Dabush, Avigail, Aubrey, Bobbi, Gazula, Harshvardhan, Feder, Amir, Doyle, Werner K, Devore, Sasha, Dugan, Patricia, Friedman, Daniel, Reichart, Roi, Brenner, Michael, Hassidim, Avinatan, Devinsky, Orrin, Flinker, Adeen, Levy, Omer, Hasson, Uri
Deep Language Models (DLMs) provide a novel computational paradigm for understanding the mechanisms of natural language processing in the human brain. Unlike traditional psycholinguistic models, DLMs use layered sequences of continuous numerical vect
Externí odkaz:
http://arxiv.org/abs/2310.07106
Autor:
Nearing, Grey, Cohen, Deborah, Dube, Vusumuzi, Gauch, Martin, Gilon, Oren, Harrigan, Shaun, Hassidim, Avinatan, Klotz, Daniel, Kratzert, Frederik, Metzger, Asher, Nevo, Sella, Pappenberger, Florian, Prudhomme, Christel, Shalev, Guy, Shenzis, Shlomo, Tekalign, Tadele, Weitzner, Dana, Matias, Yoss
Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely warnings are critical for mitigating flood risks, but hydrological simula
Externí odkaz:
http://arxiv.org/abs/2307.16104
Autor:
Lang, Oran, Yaya-Stupp, Doron, Traynis, Ilana, Cole-Lewis, Heather, Bennett, Chloe R., Lyles, Courtney, Lau, Charles, Irani, Michal, Semturs, Christopher, Webster, Dale R., Corrado, Greg S., Hassidim, Avinatan, Matias, Yossi, Liu, Yun, Hammel, Naama, Babenko, Boris
Publikováno v:
EBioMedicine 102 (2024)
AI models have shown promise in many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust in AI-based models, and could enable novel
Externí odkaz:
http://arxiv.org/abs/2306.00985
Autor:
Roit, Paul, Ferret, Johan, Shani, Lior, Aharoni, Roee, Cideron, Geoffrey, Dadashi, Robert, Geist, Matthieu, Girgin, Sertan, Hussenot, Léonard, Keller, Orgad, Momchev, Nikola, Ramos, Sabela, Stanczyk, Piotr, Vieillard, Nino, Bachem, Olivier, Elidan, Gal, Hassidim, Avinatan, Pietquin, Olivier, Szpektor, Idan
Despite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input. This phenomenon is emphasized in tasks like summarization, in which the generated summa
Externí odkaz:
http://arxiv.org/abs/2306.00186
Leximin is a common approach to multi-objective optimization, frequently employed in fair division applications. In leximin optimization, one first aims to maximize the smallest objective value; subject to this, one maximizes the second-smallest obje
Externí odkaz:
http://arxiv.org/abs/2303.12506
Autor:
Cohen, Deborah, Ryu, Moonkyung, Chow, Yinlam, Keller, Orgad, Greenberg, Ido, Hassidim, Avinatan, Fink, Michael, Matias, Yossi, Szpektor, Idan, Boutilier, Craig, Elidan, Gal
Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carry on rich open-ended conversations with humans "in the wild" remains a
Externí odkaz:
http://arxiv.org/abs/2208.02294