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

  • Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger, Truncated Marginal Neural Ratio Estimation, accepted to NeurIPS 2021, https://arxiv.org/abs/2107.01214.
  • Marco Federici, Ryota Tomioka, Patrick Forré, An Information-theoretic Approach to Distribution Shifts, accepted to 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, accepted to NeurIPS 2021, https://arxiv.org/abs/2102.05379.
  • 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.
  • Stephan Bongers, Patrick Forré, Jonas Peters, Joris M. Mooij, Foundations of Structural Causal Models with Cycles and Latent Variables, to appear in The Annals of Statistics, 2021, https://arxiv.org/abs/1611.06221.
  • 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

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