Marco Fraccaro

Publications

Highlighted publications

  • BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling.
    Lars Maaløe, Marco Fraccaro, Valentin Liévin and Ole Winther.
    Advances in Neural Information Processing Systems 32, NeurIPS 2019.
    [neurips] [pdf] [arXiv] [code] [bibtex]
  • Deep Latent Variable Models for Sequential Data.
    Marco Fraccaro.
    PhD thesis, Technical University of Denmark, 2018.
    [pdf] [bibtex]
  • Generative Temporal Models with Spatial Memory for Partially Observed Environments. [long talk]
    Marco Fraccaro, Danilo Rezende, Yori Zwols, Alexander Pritzel, S. M. Ali Eslami and Fabio Viola.
    Proceedings of the 35th International Conference on Machine Learning, ICML 2018.
    [ICML] [arXiv] [pdf] [supplementary] [bibtex]
  • A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning. [spotlight presentation]
    Marco Fraccaro*, Simon Kamronn*, Ulrich Paquet, Ole Winther.
    Advances in Neural Information Processing Systems 30, NIPS 2017.
    [NIPS] [arXiv] [pdf] [supplementary] [code] [bibtex]
  • Sequential Neural Models with Stochastic Layers. [oral presentation]
    Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet and Ole Winther.
    Advances in Neural Information Processing Systems 29, NIPS 2016.
    [NIPS] [arXiv] [pdf] [supplementary] [code] [bibtex]

Other publications

  • Performance and Agreement When Annotating Chest X-ray Text Reports—A Preliminary Step in the Development of a Deep Learning-Based Prioritization and Detection System
    Dana Li, Lea Marie Pehrson, Rasmus Bonnevie, Marco Fraccaro, Jakob Thrane, Lea Tøttrup, Carsten Ammitzbøl Lauridsen, Sedrah Butt Balaganeshan, Jelena Jankovic, Tobias Thostrup Andersen, Alyas Mayar, Kristoffer Lindskov Hansen, Jonathan Frederik Carlsen, Sune Darkner, Michael Bachmann Nielsen
    Diagnostics, 2023.
    [Diagnostics] [pdf] [bibtex]
  • Inter- and Intra-Observer Agreement When Using a Diagnostic Labeling Scheme for Annotating Findings on Chest X-rays—An Early Step in the Development of a Deep Learning-Based Decision Support System
    Dana Li, Lea Marie Pehrson, Lea Tøttrup, Marco Fraccaro, Rasmus Bonnevie, Jakob Thrane, Peter Jagd Sørensen, Alexander Rykkje, Tobias Thostrup Andersen, Henrik Steglich-Arnholm, Dorte Marianne Rohde Stærk, Lotte Borgwardt, Kristoffer Lindskov Hansen, Sune Darkner, Jonathan Frederik Carlsen, Michael Bachmann Nielsen
    Diagnostics, 2022.
    [Diagnostics] [pdf] [bibtex]
  • The Added Effect of Artificial Intelligence on Physicians’ Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review.
    Dana Li, Lea Marie Pehrson, Carsten Ammitzbøl Lauridsen, Lea Tøttrup, Marco Fraccaro, Desmond Elliott, Hubert Dariusz Zajac, Sune Darkner, Jonathan Frederik Carlsen and Michael Bachmann Nielsen.
    Diagnostics, 2021.
    [Diagnostics] [pdf] [bibtex]
  • Machine Learning meets Mathematical Optimization to predict the optimal production of offshore wind parks.
    Martina Fischetti and Marco Fraccaro.
    Computers & Operations Research, 2018.
    [pdf] [bibtex]
  • CaGeM: A Cluster Aware Deep Generative Model.
    Lars Maaløe, Marco Fraccaro and Ole Winther.
    NIPS Workshop on Advances in Approximate Bayesian Inference, 2017.
    [pdf] [arXiv] [bibtex]
  • A deep learning approach to adherence detection for type 2 diabetics.
    Ali Mohebbi, Tinna B. Aradóttir, Alexander R. Johansen, Henrik Bengtsson, Marco Fraccaro and Morten Mørup.
    Engineering in Medicine and Biology Society (EMBC), 2017.
    [pdf] [bibtex]
  • Indexable Probabilistic Matrix Factorization for Maximum Inner Product Search.
    Marco Fraccaro, Ulrich Paquet and Ole Winther.
    The Thirtieth AAAI Conference on Artificial Intelligence, 2016.
    [AAAI] [pdf] [bibtex]
  • An efficient implementation of Riemannian Manifold Hamiltonian Monte Carlo for Gaussian Process Models.
    Ulrich Paquet and Marco Fraccaro.
    Technical report, 2016.
    [pdf] [bibtex]
  • An Adaptive Resample-Move Algorithm for Estimating Normalizing Constants.
    Marco Fraccaro, Ulrich Paquet and Ole Winther.
    ArXiv preprint, 2016.
    [arXiv] [pdf] [bibtex]
  • Perturbation Theory for Variational Inference.
    Manfred Opper, Marco Fraccaro, Ulrich Paquet, Alex Susemihl and Ole Winther.
    NIPS Workshop on Advances in Approximate Bayesian Inference, 2015.
    [pdf] [bibtex]
  • Learning to Index.
    Marco Fraccaro.
    Master thesis, Technical University of Denmark, 2014.
    [pdf] [bibtex]
  • Palm area detection for reliable hand gesture recognition.
    Giulio Marin, Marco Fraccaro, Mauro Donadeo, Fabio Dominio, and Pietro Zanuttigh.
    Proceedings of IEEE International Workshop on Multimedia Signal Processing, 2013.
    [pdf] [bibtex]