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of 16
pro vyhledávání: '"Kirtland, Aaron"'
Autor:
Allen, Cameron, Kirtland, Aaron, Tao, Ruo Yu, Lobel, Sam, Scott, Daniel, Petrocelli, Nicholas, Gottesman, Omer, Parr, Ronald, Littman, Michael L., Konidaris, George
Reinforcement learning algorithms typically rely on the assumption that the environment dynamics and value function can be expressed in terms of a Markovian state representation. However, when state information is only partially observable, how can a
Externí odkaz:
http://arxiv.org/abs/2407.07333
Autor:
Gritsevskiy, Andrew, Panickssery, Arjun, Kirtland, Aaron, Kauffman, Derik, Gundlach, Hans, Gritsevskaya, Irina, Cavanagh, Joe, Chiang, Jonathan, La Roux, Lydia, Hung, Michelle
We propose a new benchmark evaluating the performance of multimodal large language models on rebus puzzles. The dataset covers 333 original examples of image-based wordplay, cluing 13 categories such as movies, composers, major cities, and food. To a
Externí odkaz:
http://arxiv.org/abs/2401.05604
Autor:
McKenzie, Ian R., Lyzhov, Alexander, Pieler, Michael, Parrish, Alicia, Mueller, Aaron, Prabhu, Ameya, McLean, Euan, Kirtland, Aaron, Ross, Alexis, Liu, Alisa, Gritsevskiy, Andrew, Wurgaft, Daniel, Kauffman, Derik, Recchia, Gabriel, Liu, Jiacheng, Cavanagh, Joe, Weiss, Max, Huang, Sicong, Droid, The Floating, Tseng, Tom, Korbak, Tomasz, Shen, Xudong, Zhang, Yuhui, Zhou, Zhengping, Kim, Najoung, Bowman, Samuel R., Perez, Ethan
Publikováno v:
Transactions on Machine Learning Research (TMLR), 10/2023, https://openreview.net/forum?id=DwgRm72GQF
Work on scaling laws has found that large language models (LMs) show predictable improvements to overall loss with increased scale (model size, training data, and compute). Here, we present evidence for the claim that LMs may show inverse scaling, or
Externí odkaz:
http://arxiv.org/abs/2306.09479
Autor:
Kirtland, Aaron, Botvinick-Greenhouse, Jonah, DeBrito, Marianne, Osborne, Megan, Johnson, Casey, Martin, Robert S., Araki, Samuel J., Eckhardt, Daniel Q.
To address noise inherent in electronic data acquisition systems and real world sources, Araki et al. [Physica D: Nonlinear Phenomena, 417 (2021) 132819] demonstrated a grid based nonlinear technique to remove noise from a chaotic signal, leveraging
Externí odkaz:
http://arxiv.org/abs/2209.05944
As quantum computing advances rapidly, guaranteeing the security of cryptographic protocols resistant to quantum attacks is paramount. Some leading candidate cryptosystems use the Learning with Errors (LWE) problem, attractive for its simplicity and
Externí odkaz:
http://arxiv.org/abs/2008.04459
We answer a question posed by Y. Elias et al. in [8] about possible spectral distortions of algebraic numbers. We provide a closed form for the spectral distortion of certain classes of cyclotomic polynomials. Moreover, we present a bound on the spec
Externí odkaz:
http://arxiv.org/abs/2007.13189
Publikováno v:
Journal of Mathematical Cryptology, Vol 16, Iss 1, Pp 215-232 (2022)
As quantum computing advances rapidly, guaranteeing the security of cryptographic protocols resistant to quantum attacks is paramount. Some leading candidate cryptosystems use the learning with errors (LWE) problem, attractive for its simplicity and
Externí odkaz:
https://doaj.org/article/0775ede9c76d4c07bc6f7bf9c7ef7798
Autor:
Kirtland, Aaron1 aaronkirtland@brown.edu, Botvinick-Greenhouse, Jonah2 jrb482@cornell.edu, DeBrito, Marianne3 marianne.debrito@gmail.com, Osborne, Megan4 osborm3@rpi.edu, Johnson, Casey5 Calyjo99@gmail.com, Martin, Robert S.6 robert.s.martin163.civ@army.mil, Araki, Samuel J.7 samuel.araki.ctr@us.af.mil, Eckhardt, Daniel Q.8 daniel.eckhardt.3@spaceforce.mil
Publikováno v:
SIAM Journal on Applied Dynamical Systems. 2023, Vol. 22 Issue 4, p2927-2944. 18p.
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Akademický článek
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