Zobrazeno 1 - 10
of 13 465
pro vyhledávání: '"Shalev A"'
Autor:
Shalev, Guy, Kratzert, Frederik
The Caravan large-sample hydrology dataset (Kratzert et al., 2023) was created to standardize and harmonize streamflow data from various regional datasets, combined with globally available meteorological forcing and catchment attributes. This communi
Externí odkaz:
http://arxiv.org/abs/2411.09459
Autor:
Agarwal, Avantika, Ben-David, Shalev
We study the quantum-classical polynomial hierarchy, QCPH, which is the class of languages solvable by a constant number of alternating classical quantifiers followed by a quantum verifier. Our main result is that QCPH is infinite relative to a rando
Externí odkaz:
http://arxiv.org/abs/2410.19062
Autor:
Ben-David, Shalev, Kundu, Srijita
We study the query complexity analogue of the class TFNP of total search problems. We give a way to convert partial functions to total search problems under certain settings; we also give a way to convert search problems back into partial functions.
Externí odkaz:
http://arxiv.org/abs/2410.16245
Autor:
Borodulin Dmitry M., Shalev Aleksey V., Safonova Elena A., Prosin Maksim V., Golovacheva Yana S., Vagaytseva Elena A.
Publikováno v:
Техника и технология пищевых производств, Vol 50, Iss 4, Pp 630-641 (2020)
Introduction. New innovative technologies make food industry more effective. The present paper introduces a new method of hopped wort production based on novel mash filters. Study objects and methods. The research featured two new designs of mash fi
Externí odkaz:
https://doaj.org/article/5bf65d88c1d84cd0842baae37f6416be
Autor:
Yu, Zhiying, Ben-David, Shalev
We study the quantum query algorithms for simplex finding, a generalization of triangle finding to hypergraphs. This problem satisfies a rank-reduction property: a quantum query algorithm for finding simplices in rank-$r$ hypergraphs can be turned in
Externí odkaz:
http://arxiv.org/abs/2409.00239
We present a novel approach for test-time adaptation via online self-training, consisting of two components. First, we introduce a statistical framework that detects distribution shifts in the classifier's entropy values obtained on a stream of unlab
Externí odkaz:
http://arxiv.org/abs/2408.07511
Autor:
Shalev-Arkushin, Rotem, Azulay, Aharon, Halperin, Tavi, Richardson, Eitan, Bermano, Amit H., Fried, Ohad
Diffusion-based generative models have recently shown remarkable image and video editing capabilities. However, local video editing, particularly removal of small attributes like glasses, remains a challenge. Existing methods either alter the videos
Externí odkaz:
http://arxiv.org/abs/2406.14510
Large language models (LLMs) have shown an impressive ability to perform tasks believed to require thought processes. When the model does not document an explicit thought process, it becomes difficult to understand the processes occurring within its
Externí odkaz:
http://arxiv.org/abs/2406.13858
Autor:
Raab, Sigal, Gat, Inbar, Sala, Nathan, Tevet, Guy, Shalev-Arkushin, Rotem, Fried, Ohad, Bermano, Amit H., Cohen-Or, Daniel
Given the remarkable results of motion synthesis with diffusion models, a natural question arises: how can we effectively leverage these models for motion editing? Existing diffusion-based motion editing methods overlook the profound potential of the
Externí odkaz:
http://arxiv.org/abs/2406.06508
Autor:
Avnat, Eden, Levy, Michal, Herstain, Daniel, Yanko, Elia, Joya, Daniel Ben, Katz, Michal Tzuchman, Eshel, Dafna, Laros, Sahar, Dagan, Yael, Barami, Shahar, Mermelstein, Joseph, Ovadia, Shahar, Shomron, Noam, Shalev, Varda, Abdulnour, Raja-Elie E.
Clinical problem-solving requires processing of semantic medical knowledge such as illness scripts and numerical medical knowledge of diagnostic tests for evidence-based decision-making. As large language models (LLMs) show promising results in many
Externí odkaz:
http://arxiv.org/abs/2406.03855