Zobrazeno 1 - 10
of 4 538
pro vyhledávání: '"Sagie, A."'
Autor:
Galun, Ran, Benaim, Sagie
Text-to-image diffusion models have demonstrated an impressive ability to produce high-quality outputs. However, they often struggle to accurately follow fine-grained spatial information in an input text. To this end, we propose a compositional appro
Externí odkaz:
http://arxiv.org/abs/2410.09792
The interconnection of lasers is pivotal across various research domains, from generating high-power lasers to studying out-of-equilibrium coupled systems. This paper explores our investigation into Hermitian coupling between lasers in an array, with
Externí odkaz:
http://arxiv.org/abs/2408.13865
Commonsense reasoning is fundamentally based on multimodal knowledge. However, existing large language models (LLMs) are primarily trained using textual data only, limiting their ability to incorporate essential visual information. In contrast, Visua
Externí odkaz:
http://arxiv.org/abs/2406.13621
Autor:
Loeschcke, Sebastian, Wang, Dan, Leth-Espensen, Christian, Belongie, Serge, Kastoryano, Michael J., Benaim, Sagie
The ability to learn compact, high-quality, and easy-to-optimize representations for visual data is paramount to many applications such as novel view synthesis and 3D reconstruction. Recent work has shown substantial success in using tensor networks
Externí odkaz:
http://arxiv.org/abs/2406.04332
We tackle the task of learning dynamic 3D semantic radiance fields given a single monocular video as input. Our learned semantic radiance field captures per-point semantics as well as color and geometric properties for a dynamic 3D scene, enabling th
Externí odkaz:
http://arxiv.org/abs/2405.19321
Autor:
Luo, Katie Z, Liu, Zhenzhen, Chen, Xiangyu, You, Yurong, Benaim, Sagie, Phoo, Cheng Perng, Campbell, Mark, Sun, Wen, Hariharan, Bharath, Weinberger, Kilian Q.
Recent advances in machine learning have shown that Reinforcement Learning from Human Feedback (RLHF) can improve machine learning models and align them with human preferences. Although very successful for Large Language Models (LLMs), these advancem
Externí odkaz:
http://arxiv.org/abs/2310.19080
We consider the task of generating diverse and realistic videos guided by natural audio samples from a wide variety of semantic classes. For this task, the videos are required to be aligned both globally and temporally with the input audio: globally,
Externí odkaz:
http://arxiv.org/abs/2309.16429
Autor:
Schwartz, Idan, Snæbjarnarson, Vésteinn, Chefer, Hila, Cotterell, Ryan, Belongie, Serge, Wolf, Lior, Benaim, Sagie
Recent advances in text-to-image diffusion models have enabled the generation of diverse and high-quality images. While impressive, the images often fall short of depicting subtle details and are susceptible to errors due to ambiguity in the input te
Externí odkaz:
http://arxiv.org/abs/2303.17155
The effect of quenched disorder in a many-body system is experimentally investigated in a controlled fashion. It is done by measuring the phase synchronization (i.e. mutual coherence) of 400 coupled lasers as a function of tunable disorder and coupli
Externí odkaz:
http://arxiv.org/abs/2303.04469
Autor:
Christy W. LaFlamme, Cassandra Rastin, Soham Sengupta, Helen E. Pennington, Sophie J. Russ-Hall, Amy L. Schneider, Emily S. Bonkowski, Edith P. Almanza Fuerte, Talia J. Allan, Miranda Perez-Galey Zalusky, Joy Goffena, Sophia B. Gibson, Denis M. Nyaga, Nico Lieffering, Malavika Hebbar, Emily V. Walker, Daniel Darnell, Scott R. Olsen, Pandurang Kolekar, Mohamed Nadhir Djekidel, Wojciech Rosikiewicz, Haley McConkey, Jennifer Kerkhof, Michael A. Levy, Raissa Relator, Dorit Lev, Tally Lerman-Sagie, Kristen L. Park, Marielle Alders, Gerarda Cappuccio, Nicolas Chatron, Leigh Demain, David Genevieve, Gaetan Lesca, Tony Roscioli, Damien Sanlaville, Matthew L. Tedder, Sachin Gupta, Elizabeth A. Jones, Monika Weisz-Hubshman, Shamika Ketkar, Hongzheng Dai, Kim C. Worley, Jill A. Rosenfeld, Hsiao-Tuan Chao, Undiagnosed Diseases Network, Geoffrey Neale, Gemma L. Carvill, University of Washington Center for Rare Disease Research, Zhaoming Wang, Samuel F. Berkovic, Lynette G. Sadleir, Danny E. Miller, Ingrid E. Scheffer, Bekim Sadikovic, Heather C. Mefford
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-21 (2024)
Abstract Sequence-based genetic testing identifies causative variants in ~ 50% of individuals with developmental and epileptic encephalopathies (DEEs). Aberrant changes in DNA methylation are implicated in various neurodevelopmental disorders but rem
Externí odkaz:
https://doaj.org/article/56ab879163324f04934308a670c8a49d
