Author of the publication

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning.

A. Immer, M. Bauer, V. Fortuin, G. Rätsch, and M. Khan. ICML, volume 139 of Proceedings of Machine Learning Research, page 4563-4573. PMLR, (2021)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Martin Immer

Verena Immer

Karl Immer

Lena Immer

Peter Immer

Other publications of authors with the same name

On the Identifiability and Estimation of Causal Location-Scale Noise Models.A. Immer, C. Schultheiss, J. Vogt, B. Schölkopf, P. Bühlmann, and A. Marx. ICML, volume 202 of Proceedings of Machine Learning Research, page 14316-14332. PMLR, (2023)Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations.A. Immer, T. van der Ouderaa, G. Rätsch, V. Fortuin, and M. van der Wilk. NeurIPS, (2022)Efficient learning of smooth probability functions from Bernoulli tests with guarantees.P. Rolland, A. Kavis, A. Immer, A. Singla, and V. Cevher. ICML, volume 97 of Proceedings of Machine Learning Research, page 5459-5467. PMLR, (2019)Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion.A. Meterez, A. Joudaki, F. Orabona, A. Immer, G. Rätsch, and H. Daneshmand. CoRR, (2023)Sub-Matrix Factorization for Real-Time Vote Prediction.A. Immer, V. Kristof, M. Grossglauser, and P. Thiran. KDD, page 2280-2290. ACM, (2020)Laplace Redux - Effortless Bayesian Deep Learning.E. Daxberger, A. Kristiadi, A. Immer, R. Eschenhagen, M. Bauer, and P. Hennig. NeurIPS, page 20089-20103. (2021)Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels.A. Immer, T. van der Ouderaa, M. van der Wilk, G. Rätsch, and B. Schölkopf. ICML, volume 202 of Proceedings of Machine Learning Research, page 14333-14352. PMLR, (2023)Hodge-Aware Contrastive Learning.A. Möllers, A. Immer, V. Fortuin, and E. Isufi. ICASSP, page 9746-9750. IEEE, (2024)Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI.T. Papamarkou, M. Skoularidou, K. Palla, L. Aitchison, J. Arbel, D. Dunson, M. Filippone, V. Fortuin, P. Hennig, J. Hernández-Lobato and 15 other author(s). ICML, OpenReview.net, (2024)Optimizing Routes of Public Transportation Systems by Analyzing the Data of Taxi Rides.K. Richly, R. Teusner, A. Immer, F. Windheuser, and L. Wolf. UrbanGIS@SIGSPATIAL, page 70-76. ACM, (2015)

BibSonomy

Disambiguation of "Immer, Alexander"

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning.

Please choose a person to relate this publication to

Martin Immer

Verena Immer

Karl Immer

Lena Immer

Peter Immer

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Immer, Alexander"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning.

Please choose a person to relate this publication to

Martin Immer

Verena Immer

Karl Immer

Lena Immer

Peter Immer

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning.