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ORCIDMatthias Bauer 0001
- > Startseite > Persons
Person information
- affiliation: DeepMind, UK
- affiliation (PhD 2019): University of Cambridge
Other persons with the same name
- Matthias Bauer — disambiguation page
- Matthias Bauer 0002 — University of Potsdam, Hasso-Plattner-Institute, Germany
- Matthias Bauer 0003 — Infineon Technologies AG, Munich, Germany (and 1 more)
- Matthias Bauer 0004 — University of Erlangen-Nuremberg, Germany
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2020 – today
- 2023
[i10]Matthias Bauer, Emilien Dupont, Andy Brock, Dan Rosenbaum, Jonathan Schwarz, Hyunjik Kim:
Spatial Functa: Scaling Functa to ImageNet Classification and Generation. CoRR abs/2302.03130 (2023)- [i9]Hyunjik Kim, Matthias Bauer, Lucas Theis, Jonathan Richard Schwarz, Emilien Dupont:
C3: High-performance and low-complexity neural compression from a single image or video. CoRR abs/2312.02753 (2023) - 2022
- [i8]Aleksander Botev, Matthias Bauer, Soham De:
Regularising for invariance to data augmentation improves supervised learning. CoRR abs/2203.03304 (2022) - 2021
- [c7]Alexander Immer, Maciej Korzepa, Matthias Bauer:
Improving predictions of Bayesian neural nets via local linearization. AISTATS 2021: 703-711 - [c6]Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan:
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. ICML 2021: 4563-4573 - [c5]Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig:
Laplace Redux - Effortless Bayesian Deep Learning. NeurIPS 2021: 20089-20103 - [i7]Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan:
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. CoRR abs/2104.04975 (2021) - [i6]Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig:
Laplace Redux - Effortless Bayesian Deep Learning. CoRR abs/2106.14806 (2021) - 2020
- [i5]Alexander Immer, Maciej Korzepa, Matthias Bauer:
Improving predictions of Bayesian neural networks via local linearization. CoRR abs/2008.08400 (2020)
2010 – 2019
- 2019
- [c4]Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard E. Turner:
Meta-Learning Probabilistic Inference for Prediction. ICLR (Poster) 2019 - 2018
- [c3]Matthias Bauer
, Valentin Volchkov, Michael Hirsch, Bernhard Schölkopf:
Automatic estimation of modulation transfer functions. ICCP 2018: 1-12 - [c2]Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman:
Learning Invariances using the Marginal Likelihood. NeurIPS 2018: 9960-9970 - [i4]Matthias Bauer, Valentin Volchkov, Michael Hirsch, Bernhard Schölkopf:
Automatic Estimation of Modulation Transfer Functions. CoRR abs/1805.01872 (2018) - [i3]Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard E. Turner:
Decision-Theoretic Meta-Learning: Versatile and Efficient Amortization of Few-Shot Learning. CoRR abs/1805.09921 (2018) - [i2]Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman:
Learning Invariances using the Marginal Likelihood. CoRR abs/1808.05563 (2018) - 2017
- [i1]Matthias Bauer, Mateo Rojas-Carulla, Jakub Bartlomiej Swiatkowski, Bernhard Schölkopf, Richard E. Turner:
Discriminative k-shot learning using probabilistic models. CoRR abs/1706.00326 (2017) - 2016
- [c1]Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen:
Understanding Probabilistic Sparse Gaussian Process Approximations. NIPS 2016: 1525-1533
Coauthor Index
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