Zobrazeno 1 - 10
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pro vyhledávání: '"Hemati P"'
Autor:
Mushtaq, Talha, Hemati, Maziar S.
Recent investigations have established the physical relevance of spatially-localized instability mechanisms in fluid dynamics and their potential for technological innovations in flow control. In this letter, we show that the mathematical problem of
Externí odkaz:
http://arxiv.org/abs/2411.05617
We have tested recently published foundation models for histopathology for image retrieval. We report macro average of F1 score for top-1 retrieval, majority of top-3 retrievals, and majority of top-5 retrievals. We perform zero-shot retrievals, i.e.
Externí odkaz:
http://arxiv.org/abs/2409.04631
Digital pathology is revolutionizing the field of pathology by enabling the digitization, storage, and analysis of tissue samples as whole slide images (WSIs). WSIs are gigapixel files that capture the intricate details of tissue samples, providing a
Externí odkaz:
http://arxiv.org/abs/2409.04615
The recently introduced structured input-output analysis is a powerful method for capturing nonlinear phenomena associated with incompressible flows, and this paper extends that method to the compressible regime. The proposed method relies upon a ref
Externí odkaz:
http://arxiv.org/abs/2407.14986
Autor:
Hemati, Hamed, Pellegrini, Lorenzo, Duan, Xiaotian, Zhao, Zixuan, Xia, Fangfang, Masana, Marc, Tscheschner, Benedikt, Veas, Eduardo, Zheng, Yuxiang, Zhao, Shiji, Li, Shao-Yuan, Huang, Sheng-Jun, Lomonaco, Vincenzo, van de Ven, Gido M.
Publikováno v:
Neural Networks, March 2025: Vol 183, 106920
Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often con
Externí odkaz:
http://arxiv.org/abs/2405.04101
Autor:
Hemati, Hamed Hematian, Beigy, Hamid
Efficiently modeling historical information is a critical component in addressing user queries within a conversational question-answering (QA) context, as historical context plays a vital role in clarifying the user's questions. However, irrelevant h
Externí odkaz:
http://arxiv.org/abs/2404.11109
Autor:
Bollmers, Laura, Babai-Hemati, Tobias, Koppitz, Boris, Eigner, Christof, Padberg, Laura, Ruesing, Michael, Eng, Lukas M., Silberhorn, Christine
Lithium niobate and lithium tantalate are among the most widespread materials for nonlinear, integrated photonics. Mixed crystals with arbitrary Nb-Ta ratios provide a new degree of freedom to tune materials properties, such as the birefringence, but
Externí odkaz:
http://arxiv.org/abs/2403.04590
Autor:
Khalil, Yasser H., Estiri, Amir H., Beitollahi, Mahdi, Asadi, Nader, Hemati, Sobhan, Li, Xu, Zhang, Guojun, Chen, Xi
In the realm of real-world devices, centralized servers in Federated Learning (FL) present challenges including communication bottlenecks and susceptibility to a single point of failure. Additionally, contemporary devices inherently exhibit model and
Externí odkaz:
http://arxiv.org/abs/2402.01863
Autor:
Beitollahi, Mahdi, Bie, Alex, Hemati, Sobhan, Brunswic, Leo Maxime, Li, Xu, Chen, Xi, Zhang, Guojun
In one-shot federated learning (FL), clients collaboratively train a global model in a single round of communication. Existing approaches for one-shot FL enhance communication efficiency at the expense of diminished accuracy. This paper introduces Fe
Externí odkaz:
http://arxiv.org/abs/2402.01862
Autor:
Hemati, Hamed, Borth, Damian
The loss function plays an important role in optimizing the performance of a learning system. A crucial aspect of the loss function is the assignment of sample weights within a mini-batch during loss computation. In the context of continual learning
Externí odkaz:
http://arxiv.org/abs/2401.15973