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
38*2020
Quasi-Monte Carlo Variational Inference
A Buchholz, F Wenzel, S Mandt
International Conference on Machine Learning (ICML), 2018
322018
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
232017
Scalable Generalized Dynamic Topic Models
P Jähnichen, F Wenzel, M Kloft, S Mandt
International Conference on Artificial Intelligence and Statistics (AISTATS), 2018
182018
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
152019
Sparse probit linear mixed model
S Mandt, F Wenzel, S Nakajima, J Cunningham, C Lippert, M Kloft
Machine Learning 106, 1621-1642, 2017
82017
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
52019
Hyperparameter ensembles for robustness and uncertainty quantification
F Wenzel, J Snoek, D Tran, R Jenatton
Neural Information Processing Systems (NeurIPS), 2020
42020
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
2*2019
Quasi-Monte Carlo Flows
F Wenzel, A Buchholz, S Mandt
NeurIPS Bayesian Deep Learning Workshop, 2018
12018
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
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
2020
Generalizing and Scaling up Dynamic Topic Models via Inducing Point Variational Inference
P Jähnichen, F Wenzel, M Kloft, S Mandt
NIPS Workshop on Advances in Approximate Bayesian Inference, 2017
2017
Scalable Approximate Inference for the Bayesian Nonlinear Support Vector Machine
F Wenzel, M Deutsch, T Galy-Fajou, M Kloft
NIPS Workshop on Advances in Approximate Bayesian Inference, 2017
2017
Finding Sparse Features in Strongly Confounded Medical Binary Data
S Mandt, U Columbia, F Wenzel, S Nakajima, J Cunningham, C Lippert, ...
NIPS Workshop on Machine Learning in Healthcare, 2015
2015
Probit Regression with Correlated Label Noise: An EM-EP approach
S Mandt, F Wenzel, J Cunningham, M Kloft
NIPS Workshop on Advances in Variational Inference, 2014
2014
Scalable Feature Extraction in Confounded Data
F Wenzel, S Mandt, M Kloft
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20