Machine Learning Meetup
This weekly seminar series, hosted by the computer vision research team at FeatureX, is open to students and professionals who share an interest in gathering with like-minded machine learning researchers. This series focuses on current and influential papers in machine learning, and brings active participants together around one relevant paper each week. The presenter will introduce the background of the paper and review the findings. Attendees are expected to have read the paper and be ready to participate in group discussions about the research content and its implications.
Space is limited and RSVP’s are mandatory for this event. Please email Emily Rogers at firstname.lastname@example.org if you plan to attend. If your plans change, please update us so we can offer space to someone else.
Paper Title and Link: Fixing a Broken ELBO by Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy
Abstract: Recent work in unsupervised representation learning has focused on learning deep directed latent-variable models. Fitting these models by maximizing the marginal likelihood or evidence is typically intractable, thus a common approximation is to maximize the evidence lower bound (ELBO) instead. However, maximum likelihood training (whether exact or approximate) does not necessarily result in a good latent representation, as we demonstrate both theoretically and empirically. In particular, we derive variational lower and upper bounds on the mutual information between the input and the latent variable, and use these bounds to derive a rate-distortion curve that characterizes the tradeoff between compression and reconstruction accuracy. Using this framework, we demonstrate that there is a family of models with identical ELBO, but different quantitative and qualitative characteristics. Our framework also suggests a simple new method to ensure that latent variable models with powerful stochastic decoders do not ignore their latent code.