Zobrazeno 1 - 10
of 8 508
pro vyhledávání: '"Or Hazan"'
Autor:
Katrin Schroeder, Vedrana Kovačević, Giuseppe Civitarese, Dimitris Velaoras, Marta Álvarez, Toste Tanhua, Loïc Jullion, Laurent Coppola, Manuel Bensi, Laura Ursella, Chiara Santinelli, Michele Giani, Jacopo Chiggiato, Mohamed Aly-Eldeen, Georgia Assimakopoulou, Giancarlo Bachi, Boie Bogner, Mireno Borghini, Vanessa Cardin, Marin Cornec, Antonia Giannakourou, Louisa Giannoudi, Alexandra Gogou, Melek Golbol, Or Hazan, Clarissa Karthäuser, Martina Kralj, Evangelia Krasakopoulou, Frano Matić, Hrvoje Mihanović, Stipe Muslim, Vassilis P. Papadopoulos, Constantine Parinos, Anne Paulitschke, Alexandra Pavlidou, Elli Pitta, Maria Protopapa, Eyal Rahav, Ofrat Raveh, Panagiotis Renieris, Nydia C. Reyes-Suarez, Eleni Rousselaki, Jacop Silverman, Ekaterini Souvermezoglou, Lidia Urbini, Christina Zeri, Soultana Zervoudaki
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-19 (2024)
Abstract The Mediterranean Sea has been sampled irregularly by research vessels in the past, mostly by national expeditions in regional waters. To monitor the hydrographic, biogeochemical and circulation changes in the Mediterranean Sea, a systematic
Externí odkaz:
https://doaj.org/article/7e6d9609530348d0aa27e52accbad40a
Diffusion Models represent a significant advancement in generative modeling, employing a dual-phase process that first degrades domain-specific information via Gaussian noise and restores it through a trainable model. This framework enables pure nois
Externí odkaz:
http://arxiv.org/abs/2411.13420
We consider the problem of length generalization in sequence prediction. We define a new metric of performance in this setting -- the Asymmetric-Regret -- which measures regret against a benchmark predictor with longer context length than available t
Externí odkaz:
http://arxiv.org/abs/2411.01035
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
Autor:
Cantalloube, Faustine, Christiaens, Valentin, Mitjans, Carles Cantero, Cioppa, Anthony, Nasedkin, Evert, Absil, Olivier, Delorme, Philippe, Wang, Jason J., Bonse, Markus J., Daglayan, Hazan, Dahlqvist, Carl-Henrik, Guyot, Nathan, Juillard, Sandrine, Mazoyer, Johan, Samland, Matthias, Sabalbal, Mariam, Ruffio, Jean-Baptiste, Van Droogenbroeck, Marc
In this communication, we report on the results of the second phase of the Exoplanet Imaging Data Challenge started in 2019. This second phase focuses on the characterization of point sources (exoplanet signals) within multispectral high-contrast ima
Externí odkaz:
http://arxiv.org/abs/2410.17636
Autor:
Daglayan, Hazan, Vary, Simon, Absil, Olivier, Cantalloube, Faustine, Christiaens, Valentin, Gillis, Nicolas, Jacques, Laurent, Leplat, Valentin, Absil, P. -A.
Effective image post-processing algorithms are vital for the successful direct imaging of exoplanets. Standard PSF subtraction methods use techniques based on a low-rank approximation to separate the rotating planet signal from the quasi-static speck
Externí odkaz:
http://arxiv.org/abs/2410.06310
In a convergence of machine learning and biology, we reveal that diffusion models are evolutionary algorithms. By considering evolution as a denoising process and reversed evolution as diffusion, we mathematically demonstrate that diffusion models in
Externí odkaz:
http://arxiv.org/abs/2410.02543
Autor:
Agarwal, Naman, Chen, Xinyi, Dogariu, Evan, Feinberg, Vlad, Suo, Daniel, Bartlett, Peter, Hazan, Elad
We address the challenge of efficient auto-regressive generation in sequence prediction models by introducing FutureFill - a method for fast generation that applies to any sequence prediction algorithm based on convolutional operators. Our approach r
Externí odkaz:
http://arxiv.org/abs/2410.03766
Autor:
Liu, Y. Isabel, Nguyen, Windsor, Devre, Yagiz, Dogariu, Evan, Majumdar, Anirudha, Hazan, Elad
This paper describes an efficient, open source PyTorch implementation of the Spectral Transform Unit. We investigate sequence prediction tasks over several modalities including language, robotics, and simulated dynamical systems. We find that for the
Externí odkaz:
http://arxiv.org/abs/2409.10489
This paper presents a Multi-modal Emotion Recognition (MER) system designed to enhance emotion recognition accuracy in challenging acoustic conditions. Our approach combines a modified and extended Hierarchical Token-semantic Audio Transformer (HTS-A
Externí odkaz:
http://arxiv.org/abs/2409.09545