Zobrazeno 1 - 10
of 134 763
pro vyhledávání: '"A. A. Bogdan"'
Autor:
E. V. Boeva, N. B. Khalezova, V. V. Rassokhin, N. G. Neznanov, E. A. Gromova, A. A. Bogdan, T. N. Trofimova, N. A. Belyakov
Publikováno v:
Обозрение психиатрии и медицинской психологии имени В.М. Бехтерева, Vol 56, Iss 4, Pp 31-44 (2022)
The purpose of the study: to give a comprehensive characteristic (clinical-immunological, psychosocial, psychopathological) of women with HIV / viral hepatitis C (HCV) co-infection, to determine the presence of structural and functional changes in th
Externí odkaz:
https://doaj.org/article/093f9230c1374c0788c83a5b78f72d8b
Autor:
Nateghi, Ramin, Zhou, Ruoji, Saft, Madeline, Schnauss, Marina, Neill, Clayton, Alam, Ridwan, Handa, Nicole, Huang, Mitchell, Li, Eric V, Goldstein, Jeffery A, Schaeffer, Edward M, Nadim, Menatalla, Pourakpour, Fattaneh, Isaila, Bogdan, Felicelli, Christopher, Mehta, Vikas, Nezami, Behtash G, Ross, Ashley, Yang, Ximing, Cooper, Lee AD
Artificial intelligence may assist healthcare systems in meeting increasing demand for pathology services while maintaining diagnostic quality and reducing turnaround time and costs. We aimed to investigate the performance of an institutionally devel
Externí odkaz:
http://arxiv.org/abs/2410.23642
The recent tension in the value of the cosmological parameter $S_8 \equiv \sigma_8(\Omega_M/0.3)^{1/2}$, which represents the amplitude of the matter density fluctuations of the universe, has not been resolved. In this work, we present constraints on
Externí odkaz:
http://arxiv.org/abs/2410.22397
With the advent of large pre-trained vision-language models such as CLIP, prompt learning methods aim to enhance the transferability of the CLIP model. They learn the prompt given few samples from the downstream task given the specific class names as
Externí odkaz:
http://arxiv.org/abs/2410.22317
We propose a novel teacher-student framework to distill knowledge from multiple teachers trained on distinct datasets. Each teacher is first trained from scratch on its own dataset. Then, the teachers are combined into a joint architecture, which fus
Externí odkaz:
http://arxiv.org/abs/2410.22184
Transformers and large language models~(LLMs) have seen rapid adoption in all domains. Their sizes have exploded to hundreds of billions of parameters and keep increasing. Under these circumstances, the training of transformers is very expensive and
Externí odkaz:
http://arxiv.org/abs/2410.21316
This paper presents a new look at the neural network (NN) robustness problem, from the point of view of graph theory analysis, specifically graph curvature. Graph curvature (e.g., Ricci curvature) has been used to analyze system dynamics and identify
Externí odkaz:
http://arxiv.org/abs/2410.19607
Track finding can be considered as a complex optimization problem initially introduced in particle physics involving the reconstruction of particle trajectories. A track is typically composed of several consecutive segments (track segments) that rese
Externí odkaz:
http://arxiv.org/abs/2410.18552
Autor:
Nowak, Aleksandra I., Mercea, Otniel-Bogdan, Arnab, Anurag, Pfeiffer, Jonas, Dauphin, Yann, Evci, Utku
Parameter-efficient transfer learning (PETL) aims to adapt pre-trained models to new downstream tasks while minimizing the number of fine-tuned parameters. Adapters, a popular approach in PETL, inject additional capacity into existing networks by inc
Externí odkaz:
http://arxiv.org/abs/2410.15858
Autor:
Laria, Héctor, Gomez-Villa, Alex, Marouf, Imad Eddine, Wang, Kai, Raducanu, Bogdan, van de Weijer, Joost
Recent advances in diffusion models have significantly enhanced image generation capabilities. However, customizing these models with new classes often leads to unintended consequences that compromise their reliability. We introduce the concept of op
Externí odkaz:
http://arxiv.org/abs/2410.14159