Zobrazeno 1 - 10
of 2 744
pro vyhledávání: '"Scalia P"'
Autor:
Edwards, Carl, Lu, Ziqing, Hajiramezanali, Ehsan, Biancalani, Tommaso, Ji, Heng, Scalia, Gabriele
Bridging biomolecular modeling with natural language information, particularly through large language models (LLMs), has recently emerged as a promising interdisciplinary research area. LLMs, having been trained on large corpora of scientific documen
Externí odkaz:
http://arxiv.org/abs/2411.00737
Autor:
Fiorenza, Patrick, Zignale, Marco, Camalleri, Marco, Scalia, Laura, Zanetti, Edoardo, Saggio, Mario, Giannazzo, Filippo, Roccaforte, Fabrizio
In this work, the effects of the duration of the post deposition annealing (PDA) in nitric oxide (NO) on the properties of SiO2/4H-SiC interfaces in n-channel lateral MOSFETs are investigated, with a special focus on the modifications of the energy p
Externí odkaz:
http://arxiv.org/abs/2410.21235
Deep neural networks excel in mapping genomic DNA sequences to associated readouts (e.g., protein-DNA binding). Beyond prediction, the goal of these networks is to reveal to scientists the underlying motifs (and their syntax) which drive genome regul
Externí odkaz:
http://arxiv.org/abs/2410.06211
Autor:
Li, Xiner, Zhao, Yulai, Wang, Chenyu, Scalia, Gabriele, Eraslan, Gokcen, Nair, Surag, Biancalani, Tommaso, Ji, Shuiwang, Regev, Aviv, Levine, Sergey, Uehara, Masatoshi
Diffusion models excel at capturing the natural design spaces of images, molecules, DNA, RNA, and protein sequences. However, rather than merely generating designs that are natural, we often aim to optimize downstream reward functions while preservin
Externí odkaz:
http://arxiv.org/abs/2408.08252
Autor:
Lu, Stephen Zhewen, Lu, Ziqing, Hajiramezanali, Ehsan, Biancalani, Tommaso, Bengio, Yoshua, Scalia, Gabriele, Koziarski, Michał
High-content phenotypic screening, including high-content imaging (HCI), has gained popularity in the last few years for its ability to characterize novel therapeutics without prior knowledge of the protein target. When combined with deep learning te
Externí odkaz:
http://arxiv.org/abs/2408.05196
Publikováno v:
Open Communications in Nonlinear Mathematical Physics, Volume 4 (October 12, 2024) ocnmp:13947
In the present paper we reconsider the integrable case of the Hamiltonian $N$-species Volterra system, as it has been introduced by Vito Volterra in 1937 and significantly enrich the results already published in the ArXiv in 2019 by two of the presen
Externí odkaz:
http://arxiv.org/abs/2407.09155
Autor:
Zhao, Yulai, Uehara, Masatoshi, Scalia, Gabriele, Biancalani, Tommaso, Levine, Sergey, Hajiramezanali, Ehsan
Diffusion models are powerful generative models that allow for precise control over the characteristics of the generated samples. While these diffusion models trained on large datasets have achieved success, there is often a need to introduce additio
Externí odkaz:
http://arxiv.org/abs/2406.12120
Autor:
Uehara, Masatoshi, Zhao, Yulai, Hajiramezanali, Ehsan, Scalia, Gabriele, Eraslan, Gökcen, Lal, Avantika, Levine, Sergey, Biancalani, Tommaso
AI-driven design problems, such as DNA/protein sequence design, are commonly tackled from two angles: generative modeling, which efficiently captures the feasible design space (e.g., natural images or biological sequences), and model-based optimizati
Externí odkaz:
http://arxiv.org/abs/2405.19673
Autor:
Uehara, Masatoshi, Zhao, Yulai, Black, Kevin, Hajiramezanali, Ehsan, Scalia, Gabriele, Diamant, Nathaniel Lee, Tseng, Alex M, Levine, Sergey, Biancalani, Tommaso
Diffusion models excel at modeling complex data distributions, including those of images, proteins, and small molecules. However, in many cases, our goal is to model parts of the distribution that maximize certain properties: for example, we may want
Externí odkaz:
http://arxiv.org/abs/2402.16359
Autor:
Uehara, Masatoshi, Zhao, Yulai, Black, Kevin, Hajiramezanali, Ehsan, Scalia, Gabriele, Diamant, Nathaniel Lee, Tseng, Alex M, Biancalani, Tommaso, Levine, Sergey
Diffusion models excel at capturing complex data distributions, such as those of natural images and proteins. While diffusion models are trained to represent the distribution in the training dataset, we often are more concerned with other properties,
Externí odkaz:
http://arxiv.org/abs/2402.15194