Florian Wenzel
Florian Wenzel
Google Brain Berlin
Bestätigte E-Mail-Adresse bei google.com - Startseite
Titel
Zitiert von
Zitiert von
Jahr
How good is the bayes posterior in deep neural networks really?
F Wenzel, K Roth, BS Veeling, J Świątkowski, L Tran, S Mandt, J Snoek, ...
International Conference of Machine Learning (ICML), 2020
93*2020
Quasi-Monte Carlo Variational Inference
A Buchholz, F Wenzel, S Mandt
International Conference on Machine Learning (ICML), 2018
372018
Hyperparameter ensembles for robustness and uncertainty quantification
F Wenzel, J Snoek, D Tran, R Jenatton
Neural Information Processing Systems (NeurIPS), 2020
342020
Bayesian Nonlinear Support Vector Machines for Big Data
F Wenzel, T Galy-Fajou, M Deutsch, M Kloft
Proceedings of the European Conference on Machine Learning and Principles …, 2017
262017
Efficient Gaussian process classification using Polya-Gamma data augmentation
F Wenzel, T Galy-Fajou, C Donner, M Kloft, M Opper
AAAI Conference on Artificial Intelligence, 2019
232019
Scalable Generalized Dynamic Topic Models
P Jähnichen, F Wenzel, M Kloft, S Mandt
International Conference on Artificial Intelligence and Statistics (AISTATS), 2018
212018
Bayesian neural network priors revisited
V Fortuin, A Garriga-Alonso, F Wenzel, G Rätsch, R Turner, ...
arXiv preprint arXiv:2102.06571, 2021
162021
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
T Galy-Fajou, F Wenzel, C Donner, M Opper
Conference on Uncertainty in Artificial Intelligence (UAI), 2019
132019
Sparse probit linear mixed model
S Mandt, F Wenzel, S Nakajima, J Cunningham, C Lippert, M Kloft
Machine Learning 106, 1621-1642, 2017
102017
On Stein Variational Neural Network Ensembles
F D'Angelo, V Fortuin, F Wenzel
arXiv preprint arXiv:2106.10760, 2021
22021
The Natural Neural Tangent Kernel: Neural Network Training Dynamics under Natural Gradient Descent
TGJ Rudner, F Wenzel, YW Teh, Y Gal
NeurIPS 2019 Workshop Bayesian Deep Learning Workshop, 2019
22019
Quasi-Monte Carlo Flows
F Wenzel, A Buchholz, S Mandt
NeurIPS Bayesian Deep Learning Workshop, 2018
22018
Scalable multi-class Gaussian process classification via data augmentation
T Galy-Fajou, F Wenzel, C Donner, M Opper
Proc. NIPS Workshop Approx. Inference, 1-12, 2018
22018
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ...
arXiv preprint arXiv:2106.04015, 2021
12021
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models
T Galy-Fajou, F Wenzel, M Opper
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
12020
Distilling Ensembles Improves Uncertainty Estimates
Z Mariet, R Jenatton, F Wenzel, D Tran
Advances in Approximate Bayesian Inference (AABI), 2020
12020
Scalable Logit Gaussian Process Classification
F Wenzel, T Galy-Fajou, C Donner, M Kloft, M Opper
NIPS Workshop on Advances in Approximate Bayesian Inference, 2017
12017
Sparse Estimation in a Correlated Probit Model
S Mandt, F Wenzel, S Nakajima, J Cunningham, C Lippert, M Kloft
stat 1050, 24, 2016
12016
Scalable Inference in Dynamic Mixture Models
P Jähnichen, F Wenzel, M Kloft
NIPS Workshop on Advances in Approximate Bayesian Inference, 2016
12016
Separating Sparse Signals from Correlated Noise in Binary Classification.
S Mandt, F Wenzel, S Nakajima, C Lippert, M Kloft
CFA@ UAI, 48-58, 2016
12016
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