Zobrazeno 1 - 10
of 20 600
pro vyhledávání: '"A, Scherrer"'
Autor:
Pallage, Julien, Scherrer, Bertrand, Naccache, Salma, Bélanger, Christophe, Lesage-Landry, Antoine
In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric. This filtering technique is conceptually interesting for integration in MLOps pipelines deploying trustworthy machine learning mo
Externí odkaz:
http://arxiv.org/abs/2410.21712
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:
Trivedi, Oem, Scherrer, Robert J.
We show that some holographic dark energy models can lead to a future evolution of the universe in which the scale factor $a$ is asymptotically constant, while $\dot a \rightarrow 0$ and the corresponding energy and pressure densities also vanish. We
Externí odkaz:
http://arxiv.org/abs/2409.11420
We use contextualized word definitions generated by large language models as semantic representations in the task of diachronic lexical semantic change detection (LSCD). In short, generated definitions are used as `senses', and the change score of a
Externí odkaz:
http://arxiv.org/abs/2406.14167
Autor:
Storm, S. David, Scherrer, Robert J.
We examine the power spectrum $P_n$ of the $n$th harmonic for Garfinkle-Vachaspati cosmic strings, which correspond to planar rectangular loops. While these loop tractories are self-intersecting, a slightly perturbed non-self-intersecting form of the
Externí odkaz:
http://arxiv.org/abs/2406.06743
A recent advance in networking is the deployment of path-aware multipath network architectures, where network endpoints are given multiple network paths to send their data on. In this work, we tackle the challenge of selecting paths for latency-sensi
Externí odkaz:
http://arxiv.org/abs/2405.04319
Forensic authorship profiling uses linguistic markers to infer characteristics about an author of a text. This task is paralleled in dialect classification, where a prediction is made about the linguistic variety of a text based on the text itself. W
Externí odkaz:
http://arxiv.org/abs/2404.18510
Autor:
Vidgen, Bertie, Agrawal, Adarsh, Ahmed, Ahmed M., Akinwande, Victor, Al-Nuaimi, Namir, Alfaraj, Najla, Alhajjar, Elie, Aroyo, Lora, Bavalatti, Trupti, Bartolo, Max, Blili-Hamelin, Borhane, Bollacker, Kurt, Bomassani, Rishi, Boston, Marisa Ferrara, Campos, Siméon, Chakra, Kal, Chen, Canyu, Coleman, Cody, Coudert, Zacharie Delpierre, Derczynski, Leon, Dutta, Debojyoti, Eisenberg, Ian, Ezick, James, Frase, Heather, Fuller, Brian, Gandikota, Ram, Gangavarapu, Agasthya, Gangavarapu, Ananya, Gealy, James, Ghosh, Rajat, Goel, James, Gohar, Usman, Goswami, Sujata, Hale, Scott A., Hutiri, Wiebke, Imperial, Joseph Marvin, Jandial, Surgan, Judd, Nick, Juefei-Xu, Felix, Khomh, Foutse, Kailkhura, Bhavya, Kirk, Hannah Rose, Klyman, Kevin, Knotz, Chris, Kuchnik, Michael, Kumar, Shachi H., Kumar, Srijan, Lengerich, Chris, Li, Bo, Liao, Zeyi, Long, Eileen Peters, Lu, Victor, Luger, Sarah, Mai, Yifan, Mammen, Priyanka Mary, Manyeki, Kelvin, McGregor, Sean, Mehta, Virendra, Mohammed, Shafee, Moss, Emanuel, Nachman, Lama, Naganna, Dinesh Jinenhally, Nikanjam, Amin, Nushi, Besmira, Oala, Luis, Orr, Iftach, Parrish, Alicia, Patlak, Cigdem, Pietri, William, Poursabzi-Sangdeh, Forough, Presani, Eleonora, Puletti, Fabrizio, Röttger, Paul, Sahay, Saurav, Santos, Tim, Scherrer, Nino, Sebag, Alice Schoenauer, Schramowski, Patrick, Shahbazi, Abolfazl, Sharma, Vin, Shen, Xudong, Sistla, Vamsi, Tang, Leonard, Testuggine, Davide, Thangarasa, Vithursan, Watkins, Elizabeth Anne, Weiss, Rebecca, Welty, Chris, Wilbers, Tyler, Williams, Adina, Wu, Carole-Jean, Yadav, Poonam, Yang, Xianjun, Zeng, Yi, Zhang, Wenhui, Zhdanov, Fedor, Zhu, Jiacheng, Liang, Percy, Mattson, Peter, Vanschoren, Joaquin
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introdu
Externí odkaz:
http://arxiv.org/abs/2404.12241
Autor:
Trivedi, Oem, Scherrer, Robert J.
Publikováno v:
Phys.Rev.D 110 (2024) 2, 023521
We explore the asymptotic future evolution of holographic dark energy (HDE) models, in which the density of the dark energy is a function of a cutoff scale $L$. We develop a general methodology to determine which models correspond to future big rip,
Externí odkaz:
http://arxiv.org/abs/2404.08912
Autor:
Giri, Anish, Scherrer, Robert J.
Publikováno v:
Phys. Rev. D, 109, 103521 (2024)
We examine big bang nucleosynthesis (BBN) in models with a time-varying gravitational constant $G$, when this time variation is rapid on the scale of the expansion rate $H$, i.e, $\dot G/G \gg H$. Such models can arise naturally in the context of sca
Externí odkaz:
http://arxiv.org/abs/2312.06525