Zobrazeno 1 - 10
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pro vyhledávání: '"Ratner ON"'
Sequential or chained models are increasingly prevalent in machine learning for scientific applications, due to their flexibility and ease of development. Chained models are particularly useful when a task is separable into distinct steps with a hier
Externí odkaz:
http://arxiv.org/abs/2411.09864
Autor:
Shoshan, Yoel, Raboh, Moshiko, Ozery-Flato, Michal, Ratner, Vadim, Golts, Alex, Weber, Jeffrey K., Barkan, Ella, Rabinovici-Cohen, Simona, Polaczek, Sagi, Amos, Ido, Shapira, Ben, Hazan, Liam, Ninio, Matan, Ravid, Sivan, Danziger, Michael M., Morrone, Joseph A., Suryanarayanan, Parthasarathy, Rosen-Zvi, Michal, Hexter, Efrat
Drug discovery typically consists of multiple steps, including identifying a target protein key to a disease's etiology, validating that interacting with this target could prevent symptoms or cure the disease, discovering a small molecule or biologic
Externí odkaz:
http://arxiv.org/abs/2410.22367
Despite the remarkable success of Large Language Models (LLMs), evaluating their outputs' quality regarding preference remains a critical challenge. Existing works usually leverage a powerful LLM (e.g., GPT4) as the judge for comparing LLMs' output p
Externí odkaz:
http://arxiv.org/abs/2410.12869
Autor:
Jamba Team, Lenz, Barak, Arazi, Alan, Bergman, Amir, Manevich, Avshalom, Peleg, Barak, Aviram, Ben, Almagor, Chen, Fridman, Clara, Padnos, Dan, Gissin, Daniel, Jannai, Daniel, Muhlgay, Dor, Zimberg, Dor, Gerber, Edden M, Dolev, Elad, Krakovsky, Eran, Safahi, Erez, Schwartz, Erez, Cohen, Gal, Shachaf, Gal, Rozenblum, Haim, Bata, Hofit, Blass, Ido, Magar, Inbal, Dalmedigos, Itay, Osin, Jhonathan, Fadlon, Julie, Rozman, Maria, Danos, Matan, Gokhman, Michael, Zusman, Mor, Gidron, Naama, Ratner, Nir, Gat, Noam, Rozen, Noam, Fried, Oded, Leshno, Ohad, Antverg, Omer, Abend, Omri, Lieber, Opher, Dagan, Or, Cohavi, Orit, Alon, Raz, Belson, Ro'i, Cohen, Roi, Gilad, Rom, Glozman, Roman, Lev, Shahar, Meirom, Shaked, Delbari, Tal, Ness, Tal, Asida, Tomer, Gal, Tom Ben, Braude, Tom, Pumerantz, Uriya, Cohen, Yehoshua, Belinkov, Yonatan, Globerson, Yuval, Levy, Yuval Peleg, Shoham, Yoav
We present Jamba-1.5, new instruction-tuned large language models based on our Jamba architecture. Jamba is a hybrid Transformer-Mamba mixture of experts architecture, providing high throughput and low memory usage across context lengths, while retai
Externí odkaz:
http://arxiv.org/abs/2408.12570
Autor:
Hsieh, Cheng-Yu, Chuang, Yung-Sung, Li, Chun-Liang, Wang, Zifeng, Le, Long T., Kumar, Abhishek, Glass, James, Ratner, Alexander, Lee, Chen-Yu, Krishna, Ranjay, Pfister, Tomas
Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input. This phenomenon has been known as the lost-in-the-middle problem. In this work
Externí odkaz:
http://arxiv.org/abs/2406.16008
Autor:
Sulc, Antonin, Bien, Alex, Eichler, Annika, Ratner, Daniel, Rehm, Florian, Mayet, Frank, Hartmann, Gregor, Hoschouer, Hayden, Tuennermann, Henrik, Kaiser, Jan, John, Jason St., Maldonado, Jennefer, Hazelwood, Kyle, Kammering, Raimund, Hellert, Thorsten, Wilksen, Tim, Kain, Verena, Hu, Wan-Lin
Electronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of logbook entries can hinder their usability and automation. As natural la
Externí odkaz:
http://arxiv.org/abs/2406.12881
Autor:
Lieber, Opher, Lenz, Barak, Bata, Hofit, Cohen, Gal, Osin, Jhonathan, Dalmedigos, Itay, Safahi, Erez, Meirom, Shaked, Belinkov, Yonatan, Shalev-Shwartz, Shai, Abend, Omri, Alon, Raz, Asida, Tomer, Bergman, Amir, Glozman, Roman, Gokhman, Michael, Manevich, Avashalom, Ratner, Nir, Rozen, Noam, Shwartz, Erez, Zusman, Mor, Shoham, Yoav
We present Jamba, a new base large language model based on a novel hybrid Transformer-Mamba mixture-of-experts (MoE) architecture. Specifically, Jamba interleaves blocks of Transformer and Mamba layers, enjoying the benefits of both model families. M
Externí odkaz:
http://arxiv.org/abs/2403.19887
Autor:
Boltz, Tobias, Martinez, Jose L., Xu, Connie, Baker, Kathryn R. L., Roussel, Ryan, Ratner, Daniel, Mustapha, Brahim, Edelen, Auralee L.
Tuning particle accelerators is a challenging and time-consuming task, but can be automated and carried out efficiently through the use of suitable optimization algorithms. With successful applications at various facilities, Bayesian optimization usi
Externí odkaz:
http://arxiv.org/abs/2403.03225
Autor:
Golts, Alex, Ratner, Vadim, Shoshan, Yoel, Raboh, Moshe, Polaczek, Sagi, Ozery-Flato, Michal, Shats, Daniel, Hazan, Liam, Ravid, Sivan, Hexter, Efrat
Bioactivity data plays a key role in drug discovery and repurposing. The resource-demanding nature of \textit{in vitro} and \textit{in vivo} experiments, as well as the recent advances in data-driven computational biochemistry research, highlight the
Externí odkaz:
http://arxiv.org/abs/2401.17174
Autor:
Chitturi, Sathya, Ramdas, Akash, Wu, Yue, Rohr, Brian, Ermon, Stefano, Dionne, Jennifer, da Jornada, Felipe H., Dunne, Mike, Tassone, Christopher, Neiswanger, Willie, Ratner, Daniel
Rapid discovery and synthesis of new materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, ma
Externí odkaz:
http://arxiv.org/abs/2312.16078