Fortuin, Vincent
- GP-VAE: Deep Probabilistic Time Series Imputation
2019/07/09 by Vincent Fortuin, Fortuin, Vincent, Dmitry Baranchuk +5 · 16 citations
Computer Science · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare
- Repulsive Deep Ensembles are Bayesian
2021/06/22 by Francesco D’Angelo, Vincent Fortuin, D'Angelo, Francesco +1 · 13 citations
Computer Science · #Generative Adversarial Networks and Image Synthesis #Adversarial Robustness in Machine Learning #Gaussian Processes and Bayesian Inference
- Priors in Bayesian Deep Learning: A Review
2021/05/14 by Vincent Fortuin, Fortuin, Vincent · 12 citations
Computer Science · #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Bayesian Neural Network Priors Revisited
2021/02/12 by Fortuin, Vincent, Garriga-Alonso, Adrià, Ober, Sebastian W. +5 · 9 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
2024/02/01 by Theodore Papamarkou, Papamarkou, Theodore, Maria Skoularidou +47 · 1 voice · 13 citations
#cs.LG #stat.ML
- SOM-VAE: Interpretable Discrete Representation Learning on Time Series
2018/06/06 by Fortuin, Vincent, Hüser, Matthias, Locatello, Francesco +2 · 6 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- A Primer on Bayesian Neural Networks: Review and Debates
2023/09/28 by Arbel, Julyan, Pitas, Konstantinos, Vladimirova, Mariia +1 · 8 citations
#Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics Theory (math.ST)
- Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
2021/04/11 by Alexander Immer, Matthias Bauer, Immer, Alexander +7 · 7 citations
Computer Science · #Gaussian Processes and Bayesian Inference #Machine Learning and Data Classification #Machine Learning and Algorithms
- On the Challenges and Opportunities in Generative AI
2024/02/28 by Laura Manduchi, Manduchi, Laura, Meister, Clara +48 · 6 citations
Computer Science · #AI-based Problem Solving and Planning #Cognitive Computing and Networks #Evolutionary Algorithms and Applications
- Can Transformers Learn Full Bayesian Inference in Context?
2025/01/28 by Arik Reuter, Reuter, Arik, Tim G. J. Rudner +5 · 8 citations
Computer Science · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG)
- Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood
2024/02/25 by Rayen Dhahri, Dhahri, Rayen, Alexander Immer +7 · 2 voices · 3 citations
Computer Science · #Neural Networks and Applications
- PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
2020/02/13 by Rothfuss, Jonas, Fortuin, Vincent, Josifoski, Martin +1 · 2 citations
#68Q32 #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Scalable Gaussian Process Variational Autoencoders
2020/10/26 by Jazbec, Metod, Ashman, Matthew, Fortuin, Vincent +3 · 2 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Exact Langevin Dynamics with Stochastic Gradients
2021/02/02 by Garriga-Alonso, Adrià, Fortuin, Vincent · 2 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Neural Variational Gradient Descent
2021/07/22 by di Langosco, Lauro Langosco, Fortuin, Vincent, Strathmann, Heiko · 2 citations
#Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
2024/07/18 by Tristan Cinquin, Cinquin, Tristan, Marvin Pförtner +7 · 1 voice · 4 citations
Computer Science · #Bayesian Methods and Mixture Models #Gaussian Processes and Bayesian Inference #cs.AI #cs.LG
- Improving Neural Additive Models with Bayesian Principles
2023/05/26 by Bouchiat, Kouroche, Immer, Alexander, Yèche, Hugo +2 · 2 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Meta-Learning Mean Functions for Gaussian Processes
2019/01/23 by Vincent Fortuin, Heiko Strathmann, Fortuin, Vincent +3 · 2 citations
Computer Science · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Data augmentation in Bayesian neural networks and the cold posterior effect
2021/06/10 by Nabarro, Seth, Ganev, Stoil, Garriga-Alonso, Adrià +3 · 1 citation
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Probing as Quantifying Inductive Bias
2021/10/15 by Alexander Immer, Immer, Alexander, Lucas Torroba Hennigen +5 · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning and Algorithms #Natural Language Processing Techniques #Topic Modeling
- Pathologies in priors and inference for Bayesian transformers
2021/10/08 by Tristan Cinquin, Cinquin, Tristan, Alexander Immer +5 · 1 citation
Computer Science · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms
- Deep Classifiers with Label Noise Modeling and Distance Awareness
2021/10/06 by Fortuin, Vincent, Collier, Mark, Wenzel, Florian +7 · 1 citation
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Understanding Pathologies of Deep Heteroskedastic Regression
2023/06/29 by Wong-Toi, Eliot, Boyd, Alex, Fortuin, Vincent +1 · 2 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
2023/04/17 by Kristiadi, Agustinus, Immer, Alexander, Eschenhagen, Runa +1 · 1 citation
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo
2023/10/30 by Szilvia Ujváry, Ujváry, Szilvia, Gergely Flamich +5 · 1 citation
Mathematics · Computer Science · #Markov Chains and Monte Carlo Methods #Statistical Methods and Inference #Bayesian Methods and Mixture Models
- Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models
2024/05/06 by Onal, Emre, Flöge, Klemens, Caldwell, Emma +2 · 1 citation
#Computation and Language (cs.CL) #FOS: Computer and information sciences
- Sparse Gaussian Neural Processes
2025/04/02 by Rochussen, Tommy, Fortuin, Vincent · 1 citation
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)