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 email@example.com if you plan to attend. If your plans change, please update us so we can offer space to someone else.
Paper Title and Link: MINE: Mutual Information Neural Estimation by Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, R Devon Hjelm
Abstract: We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks. We present a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size, trainable through back-prop, and strongly consistent. We present a handful of applications on which MINE can be used to minimize or maximize mutual information. We apply MINE to improve adversarially trained generative models. We also use MINE to implement Information Bottleneck, applying it in tasks related to supervised classification; our results demonstrate substantial improvement in flexibility and performance in these settings.