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Funnels: Exact maximum likelihood with dimensionality reduction
Samuel Klein · John Raine · Tobias Golling · Slava Voloshynovskiy · Sebastion Pina-Otey
Event URL: https://openreview.net/forum?id=zi6rD-eSqPb »
Normalizing flows are diffeomorphic, typically dimension-preserving, models trained using the likelihood of the model. We use the SurVAE framework to construct dimension reducing surjective flows via a new layer, known as the funnel. We demonstrate its efficacy on a variety of datasets, and show it improves upon or matches the performance of existing flows while having a reduced latent space size. This layer can also be used with convolutional and feed forward layers.
Author Information
Samuel Klein (University of Geneva, Switzerland)
John Raine (Univeristy of Geneva)
PostDoc on the ATLAS Experiment at CERN, working at the Unveristy of Geneva. Bringing modern ML techniques into High Energy Physics with a focus on: * Generative modelling * Graph networks * Anomaly Detection
Tobias Golling (University of Geneva)
Slava Voloshynovskiy (University of Geneva)
Sebastion Pina-Otey
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