Zobrazeno 1 - 10
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pro vyhledávání: '"Blaise, A"'
We investigate the systematic design of compliant morphing structures composed of materials reacting to an external stimulus. We add a perimeter penalty term to ensure existence of solutions. We propose a phase-field approximation of this sharp inter
Externí odkaz:
http://arxiv.org/abs/2411.06289
In this paper, we explore instabilities in binary superfluids with a nonvanishing relative superflow, particularly focusing on counterflow and coflow instabilities. We extend recent results on the thermodynamic origin of finite superflow instabilitie
Externí odkaz:
http://arxiv.org/abs/2411.01972
Autor:
Keeling, Geoff, Street, Winnie, Stachaczyk, Martyna, Zakharova, Daria, Comsa, Iulia M., Sakovych, Anastasiya, Logothesis, Isabella, Zhang, Zejia, Arcas, Blaise Agüera y, Birch, Jonathan
Pleasure and pain play an important role in human decision making by providing a common currency for resolving motivational conflicts. While Large Language Models (LLMs) can generate detailed descriptions of pleasure and pain experiences, it is an op
Externí odkaz:
http://arxiv.org/abs/2411.02432
Autor:
Cahyawijaya, Samuel, Zhang, Ruochen, Lovenia, Holy, Cruz, Jan Christian Blaise, Gilbert, Elisa, Nomoto, Hiroki, Aji, Alham Fikri
Multilingual large language models (LLMs) have gained prominence, but concerns arise regarding their reliability beyond English. This study addresses the gap in cross-lingual semantic evaluation by introducing a novel benchmark for cross-lingual sens
Externí odkaz:
http://arxiv.org/abs/2410.21573
Autor:
Meulemans, Alexander, Kobayashi, Seijin, von Oswald, Johannes, Scherrer, Nino, Elmoznino, Eric, Richards, Blake, Lajoie, Guillaume, Arcas, Blaise Agüera y, Sacramento, João
Self-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning. How can we achieve cooperation among self-interested, independent learning agents? Promising recent work has shown that in certain tasks coo
Externí odkaz:
http://arxiv.org/abs/2410.18636
Autor:
Winata, Genta Indra, Hudi, Frederikus, Irawan, Patrick Amadeus, Anugraha, David, Putri, Rifki Afina, Wang, Yutong, Nohejl, Adam, Prathama, Ubaidillah Ariq, Ousidhoum, Nedjma, Amriani, Afifa, Rzayev, Anar, Das, Anirban, Pramodya, Ashmari, Adila, Aulia, Wilie, Bryan, Mawalim, Candy Olivia, Cheng, Ching Lam, Abolade, Daud, Chersoni, Emmanuele, Santus, Enrico, Ikhwantri, Fariz, Kuwanto, Garry, Zhao, Hanyang, Wibowo, Haryo Akbarianto, Lovenia, Holy, Cruz, Jan Christian Blaise, Putra, Jan Wira Gotama, Myung, Junho, Susanto, Lucky, Machin, Maria Angelica Riera, Zhukova, Marina, Anugraha, Michael, Adilazuarda, Muhammad Farid, Santosa, Natasha, Limkonchotiwat, Peerat, Dabre, Raj, Audino, Rio Alexander, Cahyawijaya, Samuel, Zhang, Shi-Xiong, Salim, Stephanie Yulia, Zhou, Yi, Gui, Yinxuan, Adelani, David Ifeoluwa, Lee, En-Shiun Annie, Okada, Shogo, Purwarianti, Ayu, Aji, Alham Fikri, Watanabe, Taro, Wijaya, Derry Tanti, Oh, Alice, Ngo, Chong-Wah
Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate their understanding of such knowledge, we introduce WorldCuisines, a mas
Externí odkaz:
http://arxiv.org/abs/2410.12705
This paper investigates the limitations of transformers for entity-tracking tasks in large language models. We identify a theoretical constraint, showing that transformers require at least $\log_2 (n+1)$ layers to handle entity tracking with $n$ stat
Externí odkaz:
http://arxiv.org/abs/2410.05565
Publikováno v:
Sustainable Energy, Grids and Networks, 38, 101334 (2024)
In this paper, we compare the effectiveness of a two-stage control strategy for the energy management system (EMS) of a grid-connected microgrid under uncertain solar irradiance and load demand using a real-world dataset from an island in Southeast A
Externí odkaz:
http://arxiv.org/abs/2409.19568
Out-of-distribution (OOD) detection targets to detect and reject test samples with semantic shifts, to prevent models trained on in-distribution (ID) dataset from producing unreliable predictions. Existing works only extract the appearance features o
Externí odkaz:
http://arxiv.org/abs/2409.09953
Decision Trees (DTs) constitute one of the major highly non-linear AI models, valued, e.g., for their efficiency on tabular data. Learning accurate DTs is, however, complicated, especially for oblique DTs, and does take a significant training time. F
Externí odkaz:
http://arxiv.org/abs/2408.09135