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Marius Hobbhahn
Marius Hobbhahn
PhD in Machine Learning, University of Tübingen
Verified email at uni-tuebingen.de - Homepage
Title
Cited by
Cited by
Year
Compute trends across three eras of machine learning
J Sevilla, L Heim, A Ho, T Besiroglu, M Hobbhahn, P Villalobos
2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022
2022022
Will we run out of data? an analysis of the limits of scaling datasets in machine learning
P Villalobos, J Sevilla, L Heim, T Besiroglu, M Hobbhahn, A Ho
arXiv preprint arXiv:2211.04325, 2022
792022
Machine learning model sizes and the parameter gap
P Villalobos, J Sevilla, T Besiroglu, L Heim, A Ho, M Hobbhahn
arXiv preprint arXiv:2207.02852, 2022
352022
Fast predictive uncertainty for classification with bayesian deep networks
M Hobbhahn, A Kristiadi, P Hennig
Uncertainty in Artificial Intelligence, 822-832, 2022
202022
Parameter, compute and data trends in machine learning
J Sevilla, P Villalobos, JF Cerón, M Burtell, L Heim, AB Nanjajjar, A Ho, ...
2022-05-30]. https://docs. google. com/spreadsheets/d/1AAIebj …, 2021
152021
Estimating training compute of deep learning models
J Sevilla, L Heim, M Hobbhahn, T Besiroglu, A Ho, P Villalobos
Epoch, January 20, 2022
132022
Investigating causal understanding in LLMs
M Hobbhahn, T Lieberum, D Seiler
NeurIPS ML Safety Workshop, 2022
92022
Trends in GPU price-performance
M Hobbhahn, T Besiroglu
EPOCH. June 27, 2022
92022
Technical report: Large language models can strategically deceive their users when put under pressure
J Scheurer, M Balesni, M Hobbhahn
arXiv preprint arXiv:2311.07590, 2023
82023
Compute trends across three eras of machine learning. arXiv
J Sevilla, L Heim, A Ho, T Besiroglu, M Hobbhahn, P Villalobos
arXiv preprint arXiv:2202.05924, 2022
72022
Compute Trends Across Three Eras of Machine Learning.(2022)
J Sevilla, L Heim, A Ho, T Besiroglu, M Hobbhahn, P Villalobos
URL: https://arxiv. org/abs/2202.05924. doi 10, 2022
52022
Black-Box Access is Insufficient for Rigorous AI Audits
S Casper, C Ezell, C Siegmann, N Kolt, TL Curtis, B Bucknall, A Haupt, ...
arXiv preprint arXiv:2401.14446, 2024
42024
A Causal Framework for AI Regulation and Auditing
L Sharkey, CN Ghuidhir, D Braun, J Scheurer, M Balesni, L Bushnaq, ...
Preprints, 2024
12024
Laplace Matching for fast Approximate Inference in Generalized Linear Models
M Hobbhahn, P Hennig
ArXiv 2105, 2021
12021
Reflection Mechanisms as an Alignment Target: A Survey
M Hobbhahn, E Landgrebe, E Barnes
NeurIPS ML Safety Workshop, 2022
2022
Laplace Matching for fast Approximate Inference in Latent Gaussian Models
M Hobbhahn, P Hennig
arXiv preprint arXiv:2105.03109, 2021
2021
Sequence Classification using Ensembles of Recurrent Generative Expert Modules.
M Hobbhahn, MV Butz, S Fabi, S Otte
ESANN, 333-338, 2020
2020
Large Language Models can Strategically Deceive their Users when Put Under Pressure
J Scheurer, M Balesni, M Hobbhahn
ICLR 2024 Workshop on Large Language Model (LLM) Agents, 0
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