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Hobbhahn, Marius
9  Ergebnisse:
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1

Black-Box Access is Insufficient for Rigorous AI Audits:

, In: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency,
Casper, Stephen ; Ezell, Carson ; Siegmann, Charlotte... - p. 2254-2272 , 2024
Link: https://dl.acm.org/doi/1..
 
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2

Compute Trends Across Three Eras of Machine Learning:

, In: 2022 International Joint Conference on Neural Networks (IJCNN),
Sevilla, Jaime ; Heim, Lennart ; Ho, Anson... - p. 1-8 , 2022
Link: https://doi.org/10.1109/..
 
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3

Black-Box Access is Insufficient for Rigorous AI Audits:

Casper, Stephen ; Ezell, Carson ; Siegmann, Charlotte...
http://arxiv.org/abs/2401.14446.  , 2024
Link: http://arxiv.org/abs/240..
 
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4

Technical Report: Large Language Models can Strategically D..:

Scheurer, Jérémy ; Balesni, Mikita ; Hobbhahn, Marius
http://arxiv.org/abs/2311.07590.  , 2023
Link: http://arxiv.org/abs/231..
 
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5

Compute Trends Across Three Eras of Machine Learning:

Sevilla, Jaime ; Heim, Lennart ; Ho, Anson...
http://arxiv.org/abs/2202.05924.  , 2022
Link: http://arxiv.org/abs/220..
 
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6

Will we run out of data? An analysis of the limits of scali..:

Villalobos, Pablo ; Sevilla, Jaime ; Heim, Lennart...
http://arxiv.org/abs/2211.04325.  , 2022
Link: http://arxiv.org/abs/221..
 
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7

Machine Learning Model Sizes and the Parameter Gap:

Villalobos, Pablo ; Sevilla, Jaime ; Besiroglu, Tamay...
http://arxiv.org/abs/2207.02852.  , 2022
Link: http://arxiv.org/abs/220..
 
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8

Laplace Matching for fast Approximate Inference in Latent G..:

Hobbhahn, Marius ; Hennig, Philipp
http://arxiv.org/abs/2105.03109.  , 2021
Link: http://arxiv.org/abs/210..
 
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9

Fast Predictive Uncertainty for Classification with Bayesia..:

Hobbhahn, Marius ; Kristiadi, Agustinus ; Hennig, Philipp
http://arxiv.org/abs/2003.01227.  , 2020
Link: http://arxiv.org/abs/200..
 
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