138 citations
- Friedrich-Alexander-Universität Erlangen-NürnbergDE7 papers
- Technical University of MunichDE6 papers
- Centre National de la Recherche ScientifiqueFR5 papers
- University of BristolGB5 papers
- Ludwig-Maximilians-Universität MünchenDE4 papers
- Boehringer Ingelheim (Germany)DE3 papers
- Bristol Robotics LaboratoryGB3 papers
- ESPCI ParisFR3 papers
- GulliverFR3 papers
- Max Planck Institute for the Science of LightDE3 papers
- Merck KGaA, Darmstadt (Germany)DE3 papers
- Monash UniversityAU3 papers
4 papers · 1 filter
Enabling scalable clinical interpretation of ML-based phenotypes using real world data
Owen Parsons, Nathan E Barlow, Janie Baxter +4
The availability of large and deep electronic healthcare records (EHR) datasets has the potential to enable a better understanding of real-world patient journeys, and to identify n…
Distributed Deep Learning in Open Collaborations
Michael Diskin, Alexey Bukhtiyarov, Max Ryabinin +13
Modern deep learning applications require increasingly more compute to train state-of-the-art models. To address this demand, large corporations and institutions use dedicated High…
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
Robin Winter, Frank Noé, Djork-Arné Clevert
Recently, there has been great success in applying deep neural networks on graph structured data. Most work, however, focuses on either node- or graph-level supervised learning, su…
Discovering key topics from short, real-world medical inquiries via natural language processing and unsupervised learning
Angelo Ziletti, Christoph Berns, Oliver Treichel +8
Millions of unsolicited medical inquiries are received by pharmaceutical companies every year. It has been hypothesized that these inquiries represent a treasure trove of informati…