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pro vyhledávání: '"Song, Jinyeop"'
Humans distill complex experiences into fundamental abstractions that enable rapid learning and adaptation. Similarly, autoregressive transformers exhibit adaptive learning through in-context learning (ICL), which begs the question of how. In this pa
Externí odkaz:
http://arxiv.org/abs/2412.12276
Autor:
Pearce, Tim, Song, Jinyeop
Publikováno v:
TMLR 2024
Kaplan et al. [2020] (`Kaplan') and Hoffmann et al. [2022] (`Chinchilla') studied the scaling behavior of transformers trained on next-token language prediction. These studies produced different estimates for how the number of parameters ($N$) and tr
Externí odkaz:
http://arxiv.org/abs/2406.12907
Neural scaling laws characterize how model performance improves as the model size scales up. Inspired by empirical observations, we introduce a resource model of neural scaling. A task is usually composite hence can be decomposed into many subtasks,
Externí odkaz:
http://arxiv.org/abs/2402.05164
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Autor:
Kim, Geon, Ahn, Daewoong, Kang, Minhee, Jo, YoungJu, Ryu, Donghun, Kim, Hyeonjung, Song, Jinyeop, Ryu, Jea Sung, Choi, Gunho, Chung, Hyun Jung, Kim, Kyuseok, Chung, Doo Ryeon, Yoo, In Young, Huh, Hee Jae, Min, Hyunseok, Lee, Nam Yong, Park, YongKeun
For appropriate treatments of infectious diseases, rapid identification of the pathogens is crucial. Here, we developed a rapid and label-free method for identifying common bacterial pathogens as individual bacteria by using three-dimensional quantit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=sharebioRxiv::bf66186689838efec36fd5491ebcd6ee
Autor:
Ying K; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.; T. H. Chan School of Public Health, Harvard University, Boston, MA, USA., Song J; Department of Physics, MIT, Cambridge, MA, USA., Cui H; Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada.; Department of Computer Science, University of Toronto, Toronto, ON, Canada.; Vector Institute, Toronto, ON, Canada., Zhang Y; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Li S; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Chen X; Department of Computer Science, University of Toronto, Toronto, ON, Canada.; Vector Institute, Toronto, ON, Canada., Liu H; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Eames A; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., McCartney DL; Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, United Kingdom., Marioni RE; Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, United Kingdom., Poganik JR; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Moqri M; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Wang B; Department of Computer Science, University of Toronto, Toronto, ON, Canada.; Vector Institute, Toronto, ON, Canada.; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada., Gladyshev VN; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Publikováno v:
BioRxiv : the preprint server for biology [bioRxiv] 2024 Nov 04. Date of Electronic Publication: 2024 Nov 04.