Zobrazeno 1 - 10
of 22 649
pro vyhledávání: '"Ester, P."'
The quality of self-supervised pre-trained embeddings on out-of-distribution (OOD) data is poor without fine-tuning. A straightforward and simple approach to improving the generalization of pre-trained representation to OOD data is the use of deep en
Externí odkaz:
http://arxiv.org/abs/2411.13073
Understanding how viral mutant spectra organize and explore genotype space is essential for unraveling the mechanisms driving evolution at the finest scale. Here we use deep-sequencing data of an amplicon in the A2 protein of the RNA bacteriophage Q$
Externí odkaz:
http://arxiv.org/abs/2411.07110
In this paper, we study the nonparametric estimation of the density $f_\Delta$ of an increment of a L\'evy process $X$ based on $n$ observations with a sampling rate $\Delta$. The class of L\'evy processes considered is broad, including both processe
Externí odkaz:
http://arxiv.org/abs/2411.00253
Autor:
Wu, Xiangcen, Wang, Yipei, Yang, Qianye, Thorley, Natasha, Punwani, Shonit, Kasivisvanathan, Veeru, Bonmati, Ester, Hu, Yipeng
Prostate cancer diagnosis through MR imaging have currently relied on radiologists' interpretation, whilst modern AI-based methods have been developed to detect clinically significant cancers independent of radiologists. In this study, we propose to
Externí odkaz:
http://arxiv.org/abs/2410.23084
Autor:
Izadi, Ali, Ester, Martin
In this paper, we consider the problem of causal order discovery within the framework of monotonic Structural Causal Models (SCMs), which have gained attention for their potential to enable causal inference and causal discovery from observational dat
Externí odkaz:
http://arxiv.org/abs/2410.19870
Autor:
Seo, Seonghwan, Kim, Minsu, Shen, Tony, Ester, Martin, Park, Jinkyoo, Ahn, Sungsoo, Kim, Woo Youn
Generative models in drug discovery have recently gained attention as efficient alternatives to brute-force virtual screening. However, most existing models do not account for synthesizability, limiting their practical use in real-world scenarios. In
Externí odkaz:
http://arxiv.org/abs/2410.04542
Datasets often contain values that naturally reside in a metric space: numbers, strings, geographical locations, machine-learned embeddings in a Euclidean space, and so on. We study the computational complexity of repairing inconsistent databases tha
Externí odkaz:
http://arxiv.org/abs/2409.16713
Autor:
Hartung, Michael, Maier, Andreas, Delgado-Chaves, Fernando, Burankova, Yuliya, Isaeva, Olga I., Patroni, Fábio Malta de Sá, He, Daniel, Shannon, Casey, Kaufmann, Katharina, Lohmann, Jens, Savchik, Alexey, Hartebrodt, Anne, Chervontseva, Zoe, Firoozbakht, Farzaneh, Probul, Niklas, Zotova, Evgenia, Tsoy, Olga, Blumenthal, David B., Ester, Martin, Laske, Tanja, Baumbach, Jan, Zolotareva, Olga
Most complex diseases, including cancer and non-malignant diseases like asthma, have distinct molecular subtypes that require distinct clinical approaches. However, existing computational patient stratification methods have been benchmarked almost ex
Externí odkaz:
http://arxiv.org/abs/2408.00200
Autor:
Gauy, Marcelo Matheus, Koza, Natalia Hitomi, Morita, Ricardo Mikio, Stanzione, Gabriel Rocha, Junior, Arnaldo Candido, Berti, Larissa Cristina, Levin, Anna Sara Shafferman, Sabino, Ester Cerdeira, Svartman, Flaviane Romani Fernandes, Finger, Marcelo
We contrast high effectiveness of state of the art deep learning architectures designed for general audio classification tasks, refined for respiratory insufficiency (RI) detection and blood oxygen saturation (SpO$_2$) estimation and classification t
Externí odkaz:
http://arxiv.org/abs/2407.20989
The rise of cost involved with drug discovery and current speed of which they are discover, underscore the need for more efficient structure-based drug design (SBDD) methods. We employ Generative Flow Networks (GFlowNets), to effectively explore the
Externí odkaz:
http://arxiv.org/abs/2406.10867