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Akademický článek
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Autor:
Zainab Noroozali
Publikováno v:
مجله مطالعات ایرانی, Vol 22, Iss 43, Pp 549-592 (2023)
Abstract Structuralism is a theory that recognizes, studies and examines phenomena based on the rules and patterns that have created their fundamental structure; will pay. This method deals with various scientific disciplines and the phenomena in the
Externí odkaz:
https://doaj.org/article/673a270222334e95859a3055b8f3a42a
Akademický článek
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Differentially private SGD (DPSGD) enables privacy-preserving training of language models, but often reduces utility, diversity, and linguistic quality. We introduce DPRefine, a three-phase method that initializes a model using data synthesis from a
Externí odkaz:
http://arxiv.org/abs/2410.17566
Autor:
Klaver, Yvan, Morsche, Randy te, Botter, Roel A., Hashemi, Batoul, Frare, Bruno L. Segat, Mishra, Akhileshwar, Ye, Kaixuan, Mbonde, Hamidu, Ahmadi, Pooya Torab, Taleghani, Niloofar Majidian, Jonker, Evan, Braamhaar, Redlef B. G., Selvaganapathy, Ponnambalam Ravi, Mascher, Peter, van der Slot, Peter J. M., Bradley, Jonathan D. B., Marpaung, David
Seamlessly integrating stimulated Brillouin scattering (SBS) in a low-loss and mature photonic integration platform remains a complicated task. Virtually all current approaches fall short in simultaneously achieving strong SBS, low losses, and techno
Externí odkaz:
http://arxiv.org/abs/2410.16263
Autor:
Kowsher, Md, Sobuj, Md. Shohanur Islam, Prottasha, Nusrat Jahan, Alanis, E. Alejandro, Garibay, Ozlem Ozmen, Yousefi, Niloofar
Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves forecasting accuracy through the combination of multiscale time-series d
Externí odkaz:
http://arxiv.org/abs/2410.11674
We propose RoCoFT, a parameter-efficient fine-tuning method for large-scale language models (LMs) based on updating only a few rows and columns of the weight matrices in transformers. Through extensive experiments with medium-size LMs like BERT and R
Externí odkaz:
http://arxiv.org/abs/2410.10075
Autor:
Prottasha, Nusrat Jahan, Mahmud, Asif, Sobuj, Md. Shohanur Islam, Bhat, Prakash, Kowsher, Md, Yousefi, Niloofar, Garibay, Ozlem Ozmen
Large Language Models (LLMs) are gaining significant popularity in recent years for specialized tasks using prompts due to their low computational cost. Standard methods like prefix tuning utilize special, modifiable tokens that lack semantic meaning
Externí odkaz:
http://arxiv.org/abs/2410.08598
Autor:
Lu, Ximing, Sclar, Melanie, Hallinan, Skyler, Mireshghallah, Niloofar, Liu, Jiacheng, Han, Seungju, Ettinger, Allyson, Jiang, Liwei, Chandu, Khyathi, Dziri, Nouha, Choi, Yejin
Creativity has long been considered one of the most difficult aspect of human intelligence for AI to mimic. However, the rise of Large Language Models (LLMs), like ChatGPT, has raised questions about whether AI can match or even surpass human creativ
Externí odkaz:
http://arxiv.org/abs/2410.04265
Autor:
Palakkal, Jasnamol, Arzumanov, Alexey, Xie, Ruiwen, Hadaeghi, Niloofar, Wagner, Thomas, Jiang, Tianshu, Ruan, Yating, Cherkashinin, Gennady, Molina-Luna, Leopoldo, Zhang, Hongbin, Alff, Lambert
The highly conducting and transparent inorganic perovskites SrBO$_3$ with V, Nb, Mo, and their mixtures at the B-site have recently attracted the attention of the oxide electronics community as novel alternative transparent conducting oxides. For dif
Externí odkaz:
http://arxiv.org/abs/2410.01253