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pro vyhledávání: '"Pal, Koyena"'
Autor:
Mueller, Aaron, Brinkmann, Jannik, Li, Millicent, Marks, Samuel, Pal, Koyena, Prakash, Nikhil, Rager, Can, Sankaranarayanan, Aruna, Sharma, Arnab Sen, Sun, Jiuding, Todd, Eric, Bau, David, Belinkov, Yonatan
Interpretability provides a toolset for understanding how and why neural networks behave in certain ways. However, there is little unity in the field: most studies employ ad-hoc evaluations and do not share theoretical foundations, making it difficul
Externí odkaz:
http://arxiv.org/abs/2408.01416
Autor:
Fiotto-Kaufman, Jaden, Loftus, Alexander R, Todd, Eric, Brinkmann, Jannik, Juang, Caden, Pal, Koyena, Rager, Can, Mueller, Aaron, Marks, Samuel, Sharma, Arnab Sen, Lucchetti, Francesca, Ripa, Michael, Belfki, Adam, Prakash, Nikhil, Multani, Sumeet, Brodley, Carla, Guha, Arjun, Bell, Jonathan, Wallace, Byron, Bau, David
The enormous scale of state-of-the-art foundation models has limited their accessibility to scientists, because customized experiments at large model sizes require costly hardware and complex engineering that is impractical for most researchers. To a
Externí odkaz:
http://arxiv.org/abs/2407.14561
Given a set of deep learning models, it can be hard to find models appropriate to a task, understand the models, and characterize how models are different one from another. Currently, practitioners rely on manually-written documentation to understand
Externí odkaz:
http://arxiv.org/abs/2403.02327
We conjecture that hidden state vectors corresponding to individual input tokens encode information sufficient to accurately predict several tokens ahead. More concretely, in this paper we ask: Given a hidden (internal) representation of a single tok
Externí odkaz:
http://arxiv.org/abs/2311.04897
Data management has traditionally relied on synthetic data generators to generate structured benchmarks, like the TPC suite, where we can control important parameters like data size and its distribution precisely. These benchmarks were central to the
Externí odkaz:
http://arxiv.org/abs/2308.03883
Hospital discharge documentation is among the most essential, yet time-consuming documents written by medical practitioners. The objective of this study was to automatically generate hospital discharge summaries using neural network summarization mod
Externí odkaz:
http://arxiv.org/abs/2305.15222
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