BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Arthur Gretton (University College London) DTSTART:20200728T163000Z DTEND:20200728T174500Z DTSTAMP:20260423T021102Z UID:IASML/14 DESCRIPTION:Title: G eneralized Energy-Based Models\nby Arthur Gretton (University College London) as part of IAS Seminar Series on Theoretical Machine Learning\n\n\ nAbstract\nI will introduce Generalized Energy Based Models (GEBM) for gen erative modelling. These models combine two trained components: a base dis tribution (generally an implicit model)\, which can learn the support of d ata with low intrinsic dimension in a high dimensional space\; and an ener gy function\, to refine the probability mass on the learned support. Both the energy function and base jointly constitute the final model\, unlike G ANs\, which retain only the base distribution (the "generator"). In partic ular\, while the energy function is analogous to the GAN critic function\, it is not discarded after training.\nGEBMs are trained by alternating bet ween learning the energy and the base. Both training stages are well-defin ed: the energy is learned by maximising a generalized likelihood\, and the resulting energy-based loss provides informative gradients for learning t he base. Samples from the posterior on the latent space of the trained mod el can be obtained via MCMC\, thus finding regions in this space that prod uce better quality samples. Empirically\, the GEBM samples on image-genera tion tasks are of much better quality than those from the learned generato r alone\, indicating that all else being equal\, the GEBM will outperform a GAN of the same complexity. GEBMs also return state-of-the-art performan ce on density modelling tasks\, and when using base measures with an expli cit form.\n LOCATION:https://researchseminars.org/talk/IASML/14/ END:VEVENT END:VCALENDAR