Credits: Mike Mackenzie

Papers of the AI4Science Lab

Here we list all papers from collaborations with members of the AI4Science Lab: Publications and Preprints.

Publications

  • David Ruhe, Patrick Forré, Self-Supervised Inference in State-Space Models, https://arxiv.org/abs/2107.13349, accepted to ICLR 2022.
  • David Ruhe, Mark Kuiack, Antonia Rowlinson, Ralph Wijers, Patrick Forré, Detecting dispersed radio transients in real time using convolutional neural networks, Astronomy and Computing, vol. 38, 2022, https://doi.org/10.1016/j.ascom.2021.100512, https://arxiv.org/abs/2103.15418 , code.
  • Jim Boelrijk, Bob Pirok, Bernd Ensing, Patrick Forré, Bayesian optimization of comprehensive two-dimensional liquid chromatography separations, J. Chromatogr. A 2021, https://doi.org/10.1016/j.chroma.2021.462628
  • Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger, Truncated Marginal Neural Ratio Estimation, NeurIPS 2021, https://arxiv.org/abs/2107.01214.
  • Marco Federici, Ryota Tomioka, Patrick Forré, An Information-theoretic Approach to Distribution Shifts, NeurIPS 2021, https://arxiv.org/abs/2106.03783.
  • Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling, Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions, NeurIPS 2021, https://arxiv.org/abs/2102.05379.
  • Stephan Bongers, Patrick Forré, Jonas Peters, Joris M. Mooij, Foundations of Structural Causal Models with Cycles and Latent Variables, Annals of Statistics, 49(5): 2885-2915, 2021, doi:10.1214/21-AOS2064, https://arxiv.org/abs/1611.06221.
  • Andrei Apostol, Maarten Stol, Patrick Forré, Pruning by leveraging training dynamics, AI Communications, 2021, doi:10.3233/AIC-210127.
  • Maximilian Ilse, Jakub M Tomczak, Patrick Forré, Selecting Data Augmentation for Simulating Interventions, ICML 2021, https://arxiv.org/abs/2005.01856.
  • T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling, Self Normalizing Flows, ICML 2021, https://arxiv.org/abs/2011.07248.
  • Jose Gallego-Posada, Patrick Forré, Simplicial Regularization, ICLR 2021 Workshop: Geometrical and Topological Representation Learning, openreview.
  • Ferry Hooft, Alberto Pérez de Alba Ortíz, Bernd Ensing, Discovering Collective Variables of Molecular Transitions via Genetic Algorithms and Neural Networks, J. Chem. Theory Comput. 2021.
  • Benjamin Kurt Miller, Alex Cole, Gilles Louppe, Christoph Weniger, Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time, NeurIPS 2020 Workshop: Machine Learning and the Physical Sciences, https://arxiv.org/abs/2011.13951.
  • Andrei Apostol, Maarten Stol, Patrick Forré, FlipOut: Uncovering Redundant Weights via Sign Flipping, BNAIC/BeNeLearn 2020, best paper award, https://arxiv.org/abs/2009.02594.
  • Luca Falorsi, Patrick Forré, Neural Ordinary Differential Equations on Manifolds, ICML 2020 Workshop INNF+: Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, https://arxiv.org/abs/2006.06663, SlideLive.
  • Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, Zeynep Akata, Learning Robust Representations via Multi-View Information Bottleneck, ICLR 2020, https://arxiv.org/abs/2002.07017.

Preprints

  • Christoph J. Hönes, Benjamin Kurt Miller, Ana M. Heras, Bernard H. Foing, Automatically detecting anomalous exoplanet transits, https://arxiv.org/abs/2111.08679, 2021.
  • Alex Cole, Benjamin Kurt Miller, Samuel J. Witte, Maxwell X. Cai, Meiert W. Grootes, Francesco Nattino, Christoph Weniger, Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation, https://arxiv.org/abs/2111.08030, 2021
  • Patrick Forré, Quasi-Measurable Spaces, https://arxiv.org/abs/2109.11631, 2021.
  • Maurice Weiler, Patrick Forré, Erik Verlinde, Max Welling, Coordinate Independent Convolutional Networks - Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds, https://arxiv.org/abs/2106.06020, 2021.
  • Patrick Forré, Transitional Conditional Independence, https://arxiv.org/abs/2104.11547, 2021.
  • Maximilian Ilse, Patrick Forré, Max Welling, Joris M. Mooij, Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions, https://arxiv.org/abs/2103.04786, 2021.
  • Rik Helwegen, Christos Louizos, Patrick Forré, Improving Fair Predictions Using Variational Inference in Causal Models, https://arxiv.org/abs/2008.10880, 2020.
  • Stijn Verdenius, Maarten Stol, Patrick Forré, Pruning via Iterative Ranking of Sensitivity Statistics, https://arxiv.org/abs/2006.00896, 2020.

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