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pro vyhledávání: '"Gritsevskiy, Andrew"'
Autor:
Cavanagh, Joseph M., Sun, Kunyang, Gritsevskiy, Andrew, Bagni, Dorian, Bannister, Thomas D., Head-Gordon, Teresa
Here we show that a Large Language Model (LLM) can serve as a foundation model for a Chemical Language Model (CLM) which performs at or above the level of CLMs trained solely on chemical SMILES string data. Using supervised fine-tuning (SFT) and dire
Externí odkaz:
http://arxiv.org/abs/2409.02231
Autor:
Draguns, Andis, Gritsevskiy, Andrew, Motwani, Sumeet Ramesh, Rogers-Smith, Charlie, Ladish, Jeffrey, de Witt, Christian Schroeder
The rapid proliferation of open-source language models significantly increases the risks of downstream backdoor attacks. These backdoors can introduce dangerous behaviours during model deployment and can evade detection by conventional cybersecurity
Externí odkaz:
http://arxiv.org/abs/2406.02619
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:
Krenn, Mario, Buffoni, Lorenzo, Coutinho, Bruno, Eppel, Sagi, Foster, Jacob Gates, Gritsevskiy, Andrew, Lee, Harlin, Lu, Yichao, Moutinho, Joao P., Sanjabi, Nima, Sonthalia, Rishi, Tran, Ngoc Mai, Valente, Francisco, Xie, Yangxinyu, Yu, Rose, Kopp, Michael
Publikováno v:
Nature Machine Intelligence 5, 1326 (2023)
A tool that could suggest new personalized research directions and ideas by taking insights from the scientific literature could significantly accelerate the progress of science. A field that might benefit from such an approach is artificial intellig
Externí odkaz:
http://arxiv.org/abs/2210.00881
Autor:
Gritsevskiy, Andrew, Korablyov, Maksym
We propose a capsule network-based architecture for generalizing learning to new data with few examples. Using both generative and non-generative capsule networks with intermediate routing, we are able to generalize to new information over 25 times f
Externí odkaz:
http://arxiv.org/abs/1804.10172
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