Zobrazeno 1 - 10
of 15 357
pro vyhledávání: '"P. Sima"'
Autor:
Soltanpour, Sima, Chang, Arnold, Madularu, Dan, Kulkarni, Praveen, Ferris, Craig, Joslin, Chris
Functional magnetic resonance imaging (fMRI) is extensively used in clinical and preclinical settings to study brain function, however, fMRI data is inherently noisy due to physiological processes, hardware, and external noise. Denoising is one of th
Externí odkaz:
http://arxiv.org/abs/2411.19345
As an intriguing case is the goodness of the machine and deep learning models generated by these LLMs in conducting automated scientific data analysis, where a data analyst may not have enough expertise in manually coding and optimizing complex deep
Externí odkaz:
http://arxiv.org/abs/2411.18731
Autor:
Rosenberg, Gili, Brubaker, J. Kyle, Schuetz, Martin J. A., Zhu, Elton Yechao, Kadıoğlu, Serdar, Borujeni, Sima E., Katzgraber, Helmut G.
Pruning neural networks, which involves removing a fraction of their weights, can often maintain high accuracy while significantly reducing model complexity, at least up to a certain limit. We present a neural network pruning technique that builds up
Externí odkaz:
http://arxiv.org/abs/2411.17796
Conformal prediction (CP) is a distribution-free framework for achieving probabilistic guarantees on black-box models. CP is generally applied to a model post-training. Recent research efforts, on the other hand, have focused on optimizing CP efficie
Externí odkaz:
http://arxiv.org/abs/2411.01696
Data deduplication, one of the key features of modern Big Data storage devices, is the process of removing replicas of data chunks stored by different users. Despite the importance of deduplication, several drawbacks of the method, such as storage ro
Externí odkaz:
http://arxiv.org/abs/2411.01407
Autor:
Emonet, Vincent, Bolleman, Jerven, Duvaud, Severine, de Farias, Tarcisio Mendes, Sima, Ana Claudia
We introduce a Retrieval-Augmented Generation (RAG) system for translating user questions into accurate federated SPARQL queries over bioinformatics knowledge graphs (KGs) leveraging Large Language Models (LLMs). To enhance accuracy and reduce halluc
Externí odkaz:
http://arxiv.org/abs/2410.06062
Autor:
Bolleman, Jerven, Emonet, Vincent, Altenhoff, Adrian, Bairoch, Amos, Blatter, Marie-Claude, Bridge, Alan, Duvaud, Severine, Gasteiger, Elisabeth, Kuznetsov, Dmitry, Moretti, Sebastien, Michel, Pierre-Andre, Morgat, Anne, Pagni, Marco, Redaschi, Nicole, Zahn-Zabal, Monique, de Farias, Tarcisio Mendes, Sima, Ana Claudia
Background. In the last decades, several life science resources have structured data using the same framework and made these accessible using the same query language to facilitate interoperability. Knowledge graphs have seen increased adoption in bio
Externí odkaz:
http://arxiv.org/abs/2410.06010
End-effector based assistive robots face persistent challenges in generating smooth and robust trajectories when controlled by human's noisy and unreliable biosignals such as muscle activities and brainwaves. The produced endpoint trajectories are of
Externí odkaz:
http://arxiv.org/abs/2410.03486
Autor:
Bahrani, Sima, Oliveira, Romerson D., Parra-Ullauri, Juan Marcelo, Wang, Rui, Simeonidou, Dimitra
Distributed quantum computing (DQC) has emerged as a promising approach to overcome the scalability limitations of monolithic quantum processors in terms of computing capability. However, realising the full potential of DQC requires effective resourc
Externí odkaz:
http://arxiv.org/abs/2409.12675
Autor:
Ding, Kairui, Chen, Boyuan, Su, Yuchen, Gao, Huan-ang, Jin, Bu, Sima, Chonghao, Zhang, Wuqiang, Li, Xiaohui, Barsch, Paul, Li, Hongyang, Zhao, Hao
End-to-end architectures in autonomous driving (AD) face a significant challenge in interpretability, impeding human-AI trust. Human-friendly natural language has been explored for tasks such as driving explanation and 3D captioning. However, previou
Externí odkaz:
http://arxiv.org/abs/2409.06702