Vincent Fortuin

发表

GP-VAE: Deep Probabilistic Multivariate Time Series Imputation

Vincent Fortuin, Dmitry Baranchuk, 2020 .

Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models

Vincent Fortuin, 2019 .

Priors in Bayesian Deep Learning: A Review pdf

Vincent Fortuin, Vincent Fortuin, 2021, ArXiv.

Quantum Bayesian Neural Networks

Vincent Fortuin, Noah Berner, Jonas Landman, 2021, 2107.09599.

Exact Langevin Dynamics with Stochastic Gradients

Vincent Fortuin, Adrià Garriga-Alonso, 2021, ArXiv.

T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states

Gunnar Rätsch, Martin Faltys, Julia E. Vogt, 2021, CHIL.

Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations

Mark van der Wilk, Tycho F. A. van der Ouderaa, Alexander Immer, 2022, ArXiv.

Deep Classifiers with Label Noise Modeling and Distance Awareness

F. Wenzel, Rodolphe Jenatton, Dustin Tran, 2021, Trans. Mach. Learn. Res..

META$^\mathbf{2}$: Memory-efficient taxonomic classification and abundance estimation for metagenomics with deep learning.

Gunnar Rätsch, Harun Mustafa, Vincent Fortuin, 2019, 1909.13146.

Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations

Mark van der Wilk, Tycho F. A. van der Ouderaa, Alexander Immer, 2022, NeurIPS.

GP-VAE: Deep Probabilistic Time Series Imputation

Gunnar Rätsch, S. Mandt, Vincent Fortuin, 2019, AISTATS.

PAC-Bayesian Meta-Learning: From Theory to Practice

Jonas Rothfuss, Martin Josifoski, A. Krause, 2022, ArXiv.

Bayesian Neural Network Priors Revisited

Richard E. Turner, Mark van der Wilk, Gunnar Rätsch, 2021, ICLR.

SOM-VAE: Interpretable Discrete Representation Learning on Time Series pdf

Gunnar Rätsch, Francesco Locatello, Vincent Fortuin, 2019, ICLR.

Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets pdf

Gunnar Rätsch, Andreas Georgiou, Vincent Fortuin, 2019, ArXiv.

Conservative Uncertainty Estimation By Fitting Prior Networks

Richard E. Turner, Ryota Tomioka, Katja Hofmann, 2020, ICLR.

Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations

Vignesh Ram Somnath, M. Claassen, Vincent Fortuin, 2019, ICLR 2019.

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis

Vincent Fortuin, Margherita Rosnati, Vincent Fortuin, 2019, PloS one.

Supervised learning on synthetic data for reverse engineering gene regulatory networks from experimental time-series

Manfred Claassen, Max Horn, Vincent Fortuin, 2018, bioRxiv.

Meta-Learning Mean Functions for Gaussian Processes

Gunnar Rätsch, Heiko Strathmann, Vincent Fortuin, 2019, 1901.08098.

Priors in Bayesian Deep Learning: A Review

Vincent Fortuin, 2021, International Statistical Review.

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning pdf

Gunnar Ratsch, Alexander Immer, Vincent Fortuin, 2021, ICML.

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees pdf

Andreas Krause, Vincent Fortuin, Jonas Rothfuss, 2020, ICML.

Neural Variational Gradient Descent pdf

Vincent Fortuin, Heiko Strathmann, Lauro Langosco di Langosco, 2021, ArXiv.

Incorporating Unlabelled Data into Bayesian Neural Networks

Y. Teh, Mrinank Sharma, Tom Rainforth, 2023, ArXiv.

Repulsive Deep Ensembles are Bayesian

Vincent Fortuin, Francesco D'Angelo, 2021, NeurIPS.

On Interpretable Reranking-Based Dependency Parsing Systems

E. Ponti, Vincent Fortuin, Ryan Cotterell, 2023 .

Scalable Gaussian Process Variational Autoencoders

Stephan Mandt, Gunnar Ratsch, Vincent Fortuin, 2020, AISTATS.

Sparse Gaussian Process Variational Autoencoders pdf

Richard E. Turner, Michael Pearce, Vincent Fortuin, 2020, ArXiv.

On Disentanglement in Gaussian Process Variational Autoencoders pdf

Gunnar Ratsch, Vincent Fortuin, Simon Bing, 2021, ArXiv.

SOM-VAE: Interpretable Discrete Representation Learning on Time Series

Gunnar Rätsch, Francesco Locatello, Matthias Hüser, 2018, ICLR 2018.

Sparse Gaussian Processes on Discrete Domains pdf

Gunnar Rätsch, Vincent Fortuin, Heiko Strathmann, 2018, IEEE Access.

Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference

G. Rätsch, Alexander Immer, Vincent Fortuin, 2023, ArXiv.

Probing as Quantifying Inductive Bias

Lucas Torroba Hennigen, Alexander Immer, Vincent Fortuin, 2021, ACL.

On Stein Variational Neural Network Ensembles pdf

Florian Wenzel, Vincent Fortuin, Francesco D'Angelo, 2021, ArXiv.

Data augmentation in Bayesian neural networks and the cold posterior effect pdf

Seth Nabarro, Mark van der Wilk, Laurence Aitchison, 2021, UAI.

BNNpriors: A library for Bayesian neural network inference with different prior distributions

Mark van der Wilk, Laurence Aitchison, Vincent Fortuin, 2021, Softw. Impacts.

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization

Agustinus Kristiadi, Alexander Immer, Vincent Fortuin, 2023, ArXiv.

Pathologies in priors and inference for Bayesian transformers pdf

Max Horn, Alexander Immer, Vincent Fortuin, 2021, ArXiv.

A Bayesian Approach to Invariant Deep Neural Networks pdf

Mark van der Wilk, Marco Federici, Vincent Fortuin, 2021, ArXiv.

InspireMe: Learning Sequence Models for Stories

Markus H. Gross, Sasha Schriber, Vincent Fortuin, 2018, AAAI.

Sparse MoEs meet Efficient Ensembles

Carlos Riquelme Ruiz, F. Wenzel, Jasper Snoek, 2021, Trans. Mach. Learn. Res..

Multivariate Time Series Imputation with Variational Autoencoders pdf

Gunnar Rätsch, Stephan Mandt, Vincent Fortuin, 2019, ArXiv.

Annealed Stein Variational Gradient Descent

Vincent Fortuin, Francesco D'Angelo, 2021, ArXiv.

Scalable Gaussian Process Variational Autoencoders.

Richard E. Turner, Will Tebbutt, Vincent Fortuin, 2020 .

Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks

Alexander Immer, Vincent Fortuin, E. Isufi, 2023, ArXiv.

Improving Neural Additive Models with Bayesian Principles

G. Rätsch, Alexander Immer, Vincent Fortuin, 2023, 2305.16905.

Understanding Pathologies of Deep Heteroskedastic Regression

S. Mandt, Alex Boyd, Vincent Fortuin, 2023, ArXiv.

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees pdf

Andreas Krause, Vincent Fortuin, Jonas Rothfuss, 2020, ICML.

Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data

Vignesh Ram Somnath, M. Claassen, Vincent Fortuin, 2021, PLoS Comput. Biol..

Factorized Gaussian Process Variational Autoencoders pdf

Michael Pearce, Vincent Fortuin, Metod Jazbec, 2020, ArXiv.

Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations

Mark van der Wilk, Tycho F. A. van der Ouderaa, Alexander Immer, 2022, NeurIPS.

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning

M. E. Khan, Gunnar Rätsch, M. Bauer, 2021, ICML.

Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice

Jonas Rothfuss, Martin Josifoski, A. Krause, 2022, 2211.07206.

DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps

Gunnar Rätsch, Vincent Fortuin, Matthias Hüser, 2019 .

A Primer on Bayesian Neural Networks: Review and Debates

Vincent Fortuin, M. Vladimirova, Konstantinos Pitas, 2023, ArXiv.