Zobrazeno 1 - 10
of 5 757
pro vyhledávání: '"Ohana, A"'
Autor:
Ohana, Ruben, McCabe, Michael, Meyer, Lucas, Morel, Rudy, Agocs, Fruzsina J., Beneitez, Miguel, Berger, Marsha, Burkhart, Blakesley, Dalziel, Stuart B., Fielding, Drummond B., Fortunato, Daniel, Goldberg, Jared A., Hirashima, Keiya, Jiang, Yan-Fei, Kerswell, Rich R., Maddu, Suryanarayana, Miller, Jonah, Mukhopadhyay, Payel, Nixon, Stefan S., Shen, Jeff, Watteaux, Romain, Blancard, Bruno Régaldo-Saint, Rozet, François, Parker, Liam H., Cranmer, Miles, Ho, Shirley
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the effi
Externí odkaz:
http://arxiv.org/abs/2412.00568
We present the results of four magnetohydrodynamic simulations and one alpha-disk simulation of accretion disks in a compact binary system, neglecting vertical stratification and assuming a locally isothermal equation of state. We demonstrate that in
Externí odkaz:
http://arxiv.org/abs/2411.15325
Autor:
Barak, Ohana
We study the homomorphisms from a fixed finitely generated group to strictly acylindrical colorable hierarchically hyperbolic groups. We prove that any such group is equationally noetherian.
Comment: 37 pages, 2 figures
Comment: 37 pages, 2 figures
Externí odkaz:
http://arxiv.org/abs/2410.00977
Optical training of large-scale Transformers and deep neural networks with direct feedback alignment
Autor:
Wang, Ziao, Müller, Kilian, Filipovich, Matthew, Launay, Julien, Ohana, Ruben, Pariente, Gustave, Mokaadi, Safa, Brossollet, Charles, Moreau, Fabien, Cappelli, Alessandro, Poli, Iacopo, Carron, Igor, Daudet, Laurent, Krzakala, Florent, Gigan, Sylvain
Modern machine learning relies nearly exclusively on dedicated electronic hardware accelerators. Photonic approaches, with low consumption and high operation speed, are increasingly considered for inference but, to date, remain mostly limited to rela
Externí odkaz:
http://arxiv.org/abs/2409.12965
Autor:
Lefort, Baptiste, Benhamou, Eric, Ohana, Jean-Jacques, Guez, Beatrice, Saltiel, David, Jacquot, Thomas
This paper explores the application of the Condorcet Jury theorem to the domain of sentiment analysis, specifically examining the performance of various large language models (LLMs) compared to simpler natural language processing (NLP) models. The th
Externí odkaz:
http://arxiv.org/abs/2409.00094
In this paper, we demonstrate that non-generative, small-sized models such as FinBERT and FinDRoBERTa, when fine-tuned, can outperform GPT-3.5 and GPT-4 models in zero-shot learning settings in sentiment analysis for financial news. These fine-tuned
Externí odkaz:
http://arxiv.org/abs/2409.11408
Autor:
Ohana, Tal Shahar, Guendelman, Gabriel, Mishuk, Eran, Kandel, Nadav, Garti, Dror, Gurevich, Doron, Bitton, Ora, Dayan, Barak
Of the many applications of whispering-gallery mode (WGM) microresonators, Single-atom cavity-QED poses the most extreme demands on mode-volume and quality factor. Here we present a model-based procedure for the fabrication of small mode-volume ultra
Externí odkaz:
http://arxiv.org/abs/2408.08257
Autor:
Ifland, Beni, Duani, Elad, Krief, Rubin, Ohana, Miro, Zilberman, Aviram, Murillo, Andres, Manor, Ofir, Lavi, Ortal, Kenji, Hikichi, Shabtai, Asaf, Elovici, Yuval, Puzis, Rami
Communication network engineering in enterprise environments is traditionally a complex, time-consuming, and error-prone manual process. Most research on network engineering automation has concentrated on configuration synthesis, often overlooking ch
Externí odkaz:
http://arxiv.org/abs/2407.08249
Autor:
Golkar, Siavash, Bietti, Alberto, Pettee, Mariel, Eickenberg, Michael, Cranmer, Miles, Hirashima, Keiya, Krawezik, Geraud, Lourie, Nicholas, McCabe, Michael, Morel, Rudy, Ohana, Ruben, Parker, Liam Holden, Blancard, Bruno Régaldo-Saint, Cho, Kyunghyun, Ho, Shirley
Transformers have revolutionized machine learning across diverse domains, yet understanding their behavior remains crucial, particularly in high-stakes applications. This paper introduces the contextual counting task, a novel toy problem aimed at enh
Externí odkaz:
http://arxiv.org/abs/2406.02585
Autor:
Lefort, Baptiste, Benhamou, Eric, Ohana, Jean-Jacques, Saltiel, David, Guez, Beatrice, Jacquot, Thomas
This paper introduces a new risk-on risk-off strategy for the stock market, which combines a financial stress indicator with a sentiment analysis done by ChatGPT reading and interpreting Bloomberg daily market summaries. Forecasts of market stress de
Externí odkaz:
http://arxiv.org/abs/2404.00012