Patrick Forré

According to our database, Patrick Forré authored at least 45 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Clifford-Steerable Convolutional Neural Networks.

Maksim Zhdanov, David Ruhe, Maurice Weiler, Ana Lucic, Johannes Brandstetter, Patrick Forré

CoRR, 2024

Clifford Group Equivariant Simplicial Message Passing Networks.

Cong Liu, David Ruhe, Floor Eijkelboom, Patrick Forré

CoRR, 2024

2023

Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data.

Jim Boelrijk, Denice van Herwerden, Bernd Ensing, Patrick Forré, Saer Samanipour

J. Cheminformatics, December, 2023

Designing Long-term Group Fair Policies in Dynamical Systems.

Miriam Rateike, Isabel Valera, Patrick Forré

CoRR, 2023

Anytime-Valid Confidence Sequences for Consistent Uncertainty Estimation in Early-Exit Neural Networks.

Metod Jazbec, Patrick Forré, Stephan Mandt, Dan Zhang, Eric T. Nalisnick

CoRR, 2023

Deep anytime-valid hypothesis testing.

Teodora Pandeva, Patrick Forré, Aaditya Ramdas, Shubhanshu Shekhar

CoRR, 2023

Lie Group Decompositions for Equivariant Neural Networks.

Mircea Mironenco, Patrick Forré

CoRR, 2023

Simulation-based Inference with the Generalized Kullback-Leibler Divergence.

Benjamin Kurt Miller, Marco Federici, Christoph Weniger, Patrick Forré

CoRR, 2023

Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck.

Marco Federici, Patrick Forré, Ryota Tomioka, Bastiaan S. Veeling

CoRR, 2023

On the Effectiveness of Hybrid Mutual Information Estimation.

Marco Federici, David Ruhe, Patrick Forré

CoRR, 2023

Balancing Simulation-based Inference for Conservative Posteriors.

Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré, Christoph Weniger, Gilles Louppe

CoRR, 2023

Multi-View Independent Component Analysis with Shared and Individual Sources.

Teodora Pandeva, Patrick Forré

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Clifford Group Equivariant Neural Networks.

David Ruhe, Johannes Brandstetter, Patrick Forré

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems.

Fiona Lippert, Bart Kranstauber, Emiel van Loon, Patrick Forré

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Equivariance-aware Architectural Optimization of Neural Networks.

Kaitlin Maile, Dennis George Wilson, Patrick Forré

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Multi-objective optimization via equivariant deep hypervolume approximation.

Jim Boelrijk, Bernd Ensing, Patrick Forré

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study.

David Ruhe, Kaze Wong, Miles D. Cranmer, Patrick Forré

CoRR, 2022

Physics-informed inference of aerial animal movements from weather radar data.

Fiona Lippert, Bart Kranstauber, E. Emiel van Loon, Patrick Forré

CoRR, 2022

E-Valuating Classifier Two-Sample Tests.

Teodora Pandeva, Tim Bakker, Christian A. Naesseth, Patrick Forré

CoRR, 2022

Architectural Optimization over Subgroups for Equivariant Neural Networks.

Kaitlin Maile, Dennis G. Wilson, Patrick Forré

CoRR, 2022

Information Decomposition Diagrams Applied beyond Shannon Entropy: A Generalization of Hu's Theorem.

Leon Lang, Pierre Baudot, Rick Quax, Patrick Forré

CoRR, 2022

Detecting dispersed radio transients in real time using convolutional neural networks.

David Ruhe, Mark Kuiack, Antonia Rowlinson, Ralph A. M. J. Wijers, Patrick Forré

Astron. Comput., 2022

Pruning by leveraging training dynamics.

Andrei C. Apostol, Maarten C. Stol, Patrick Forré

AI Commun., 2022

Contrastive Neural Ratio Estimation.

Benjamin Kurt Miller, Christoph Weniger, Patrick Forré

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Self-Supervised Inference in State-Space Models.

David Ruhe, Patrick Forré

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Self-Supervised Hybrid Inference in State-Space Models.

David Ruhe, Patrick Forré

CoRR, 2021

Coordinate Independent Convolutional Networks - Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds.

Maurice Weiler, Patrick Forré, Erik Verlinde, Max Welling

CoRR, 2021

Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions.

Maximilian Ilse, Patrick Forré, Max Welling, Joris M. Mooij

CoRR, 2021

Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models.

Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling

CoRR, 2021

Truncated Marginal Neural Ratio Estimation.

Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions.

Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

An Information-theoretic Approach to Distribution Shifts.

Marco Federici, Ryota Tomioka, Patrick Forré

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Self Normalizing Flows.

T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling

Proceedings of the 38th International Conference on Machine Learning, 2021

Selecting Data Augmentation for Simulating Interventions.

Maximilian Ilse, Jakub M. Tomczak, Patrick Forré

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Improving Fair Predictions Using Variational Inference In Causal Models.

Rik Helwegen, Christos Louizos, Patrick Forré

CoRR, 2020

Neural Ordinary Differential Equations on Manifolds.

Luca Falorsi, Patrick Forré

CoRR, 2020

Pruning via Iterative Ranking of Sensitivity Statistics.

Stijn Verdenius, Maarten Stol, Patrick Forré

CoRR, 2020

Designing Data Augmentation for Simulating Interventions.

Maximilian Ilse, Jakub M. Tomczak, Patrick Forré

CoRR, 2020

Learning Robust Representations via Multi-View Information Bottleneck.

Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, Zeynep Akata

Proceedings of the 8th International Conference on Learning Representations, 2020

FlipOut: Uncovering Redundant Weights via Sign Flipping.

Andrei C. Apostol, Maarten C. Stol, Patrick Forré

Proceedings of the Artificial Intelligence and Machine Learning - 32nd Benelux Conference, 2020

2019

Sinkhorn AutoEncoders.

Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias.

Patrick Forré, Joris M. Mooij

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Reparameterizing Distributions on Lie Groups.

Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Explorations in Homeomorphic Variational Auto-Encoding.

Luca Falorsi, Pim de Haan, Tim R. Davidson, Nicola De Cao, Maurice Weiler, Patrick Forré, Taco S. Cohen

CoRR, 2018

Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders.

Patrick Forré, Joris M. Mooij

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018