Zobrazeno 1 - 10
of 22 732
pro vyhledávání: '"P., Gal"'
The practical use of text-to-image generation has evolved from simple, monolithic models to complex workflows that combine multiple specialized components. While workflow-based approaches can lead to improved image quality, crafting effective workflo
Externí odkaz:
http://arxiv.org/abs/2410.01731
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:
Lutsker, Guy, Sapir, Gal, Godneva, Anastasia, Shilo, Smadar, Greenfield, Jerry R, Samocha-Bonet, Dorit, Mannor, Shie, Meirom, Eli, Chechik, Gal, Rossman, Hagai, Segal, Eran
Recent advances in self-supervised learning enabled novel medical AI models, known as foundation models (FMs) that offer great potential for characterizing health from diverse biomedical data. Continuous glucose monitoring (CGM) provides rich, tempor
Externí odkaz:
http://arxiv.org/abs/2408.11876
Autor:
Andriushchenko, Maksym, Souly, Alexandra, Dziemian, Mateusz, Duenas, Derek, Lin, Maxwell, Wang, Justin, Hendrycks, Dan, Zou, Andy, Kolter, Zico, Fredrikson, Matt, Winsor, Eric, Wynne, Jerome, Gal, Yarin, Davies, Xander
The robustness of LLMs to jailbreak attacks, where users design prompts to circumvent safety measures and misuse model capabilities, has been studied primarily for LLMs acting as simple chatbots. Meanwhile, LLM agents -- which use external tools and
Externí odkaz:
http://arxiv.org/abs/2410.09024
A crucial capability of Machine Learning models in real-world applications is the ability to continuously learn new tasks. This adaptability allows them to respond to potentially inevitable shifts in the data-generating distribution over time. Howeve
Externí odkaz:
http://arxiv.org/abs/2410.07812
The phenomenon of benign overfitting, where a trained neural network perfectly fits noisy training data but still achieves near-optimal test performance, has been extensively studied in recent years for linear models and fully-connected/convolutional
Externí odkaz:
http://arxiv.org/abs/2410.07746
We study what provable privacy attacks can be shown on trained, 2-layer ReLU neural networks. We explore two types of attacks; data reconstruction attacks, and membership inference attacks. We prove that theoretical results on the implicit bias of 2-
Externí odkaz:
http://arxiv.org/abs/2410.07632
Autor:
Beitia-Antero, L., Fuente, A., Navarro-Almaida, D., de Castro, A. I. Gómez, Wakelam, V., Caselli, P., Gal, R. Le, Esplugues, G., Rivière-Marichalar, P., Spezzano, S., Pineda, J. E., Rodríguez-Baras, M., Canet, A., Martín-Doménech, R., Roncero, O.
Publikováno v:
Astronomy & Astrophysics 2024, Volume 688, id.A188, 17 pp
(Abridged) We explore the chemistry of the most abundant C, O, S, and N bearing species in molecular clouds, in the context of the IRAM 30 m Large Programme Gas phase Elemental abundances in Molecular Clouds (GEMS). In this work, we aim to assess the
Externí odkaz:
http://arxiv.org/abs/2410.04226
This paper introduces Idempotent Test-Time Training (IT$^3$), a novel approach to addressing the challenge of distribution shift. While supervised-learning methods assume matching train and test distributions, this is rarely the case for machine lear
Externí odkaz:
http://arxiv.org/abs/2410.04201
We introduce a novel platform for realizing interaction-induced Hall crystals with diverse Chern numbers $C$. This platform consists of a two-dimensional semiconductor or graphene subjected to an out-of-plane magnetic field and a one-dimensional modu
Externí odkaz:
http://arxiv.org/abs/2410.03888