Zobrazeno 1 - 10
of 141 548
pro vyhledávání: '"P, Pace"'
Autor:
Rolland Romain
Dramaticky pojaté pásmo o osudovém přátelství R. Rolanda a spisovatelky Malwidy von Meysenburg, jež významně zasáhla do myšlení a tvorby francouzského humanisty.
PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices
Autor:
Zhu, Hanqing, Cong, Wenyan, Chen, Guojin, Ning, Shupeng, Chen, Ray T., Gu, Jiaqi, Pan, David Z.
Electromagnetic field simulation is central to designing, optimizing, and validating photonic devices and circuits. However, costly computation associated with numerical simulation poses a significant bottleneck, hindering scalability and turnaround
Externí odkaz:
http://arxiv.org/abs/2411.03527
This is a short description of our solver OSCM submitted by our team MPPEG to the PACE 2024 challenge both for the exact track and the parameterized track, available at https://github.com/pauljngr/PACE2024 and https://doi.org/10.5281/zenodo.11546972.
Externí odkaz:
http://arxiv.org/abs/2412.00292
Autor:
Hus Jan
Kázání M. Jana Husa, které zamýšlel přednést na kostnickém koncilu. Text prvního slavnostního kázání, které mu nikdy nebylo dáno proslovit. Řeč o míru vychází z perikopy čtené zpravidla o svátku Šimona a Judy, který v roce 1
We consider online convex optimization with time-varying constraints and conduct performance analysis using two stringent metrics: dynamic regret with respect to the online solution benchmark, and hard constraint violation that does not allow any com
Externí odkaz:
http://arxiv.org/abs/2412.10703
Parameter-Efficient Fine-Tuning (PEFT) effectively adapts pre-trained vision transformers to downstream tasks. However, the optimization for tasks performance often comes at the cost of generalizability in fine-tuned models. To address this issue, we
Externí odkaz:
http://arxiv.org/abs/2409.17137
Publikováno v:
SIGMOD 2024
Cardinality estimation (CE) plays a crucial role in database optimizer. We have witnessed the emergence of numerous learned CE models recently which can outperform traditional methods such as histograms and samplings. However, learned models also bri
Externí odkaz:
http://arxiv.org/abs/2409.15990
Autor:
Yang, Yingxuan, Wang, Huayi, Wen, Muning, Mo, Xiaoyun, Peng, Qiuying, Wang, Jun, Zhang, Weinan
In the rapidly advancing field of Large Language Models (LLMs), effectively leveraging existing datasets during fine-tuning to maximize the model's potential is of paramount importance. This paper introduces P3, an adaptive framework aimed at optimiz
Externí odkaz:
http://arxiv.org/abs/2408.05541
Autor:
Aroke EN, Nagidi JG, Srinivasasainagendra V, Quinn TL, Agbor FBAT, Kinnie KR, Tiwari HK, Goodin BR
Publikováno v:
Journal of Pain Research, Vol Volume 17, Pp 4317-4329 (2024)
Edwin N Aroke,1,* Jai Ganesh Nagidi,2 Vinodh Srinivasasainagendra,3 Tammie L Quinn,4 Fiona BAT Agbor,1 Kiari R Kinnie,1 Hemant K Tiwari,3 Burel R Goodin5,* 1Department of Acute, Chronic, and Continuing Care, School of Nursing, University of A
Externí odkaz:
https://doaj.org/article/6a1d3615d8d340cd861a8f4b186f664b
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