Seminars


The Priberam Machine Learning Lunch Seminars are a series of informal meetings which occur every two weeks at Instituto Superior Técnico, in Lisbon. It works as a discussion forum involving different research groups, from IST and elsewhere. Its participants are interested in areas such as (but not limited to): statistical machine learning, signal processing, pattern recognition, computer vision, natural language processing, computational biology, neural networks, control systems, reinforcement learning, or anything related (even if vaguely) with machine learning.

The seminars last for about one hour (including time for discussion and questions) and revolve around the general topic of Machine Learning. The speaker is a volunteer who decides the topic of his/her presentation. Past seminars have included presentations about state-of-the-art research, surveys and tutorials, practicing a conference talk, presenting a challenging problem and asking for help, and illustrating an interesting application of Machine Learning such as a prototype or finished product.

Presenters can have any background: undergrads, graduate students, academic researchers, company staff, etc. Anyone is welcome both to attend the seminar as well as to present it. Ocasionally we will have invited speakers. See below for a list of all seminars, including the speakers, titles and abstracts.

Note: The seminars are held at lunch-time, and include delicious free food.

Feel free to join our mailing list, where seminar topics are announced beforehand. You may also visit the group webpage. Anyone can attend the seminars. If you would like to present something, please send us an email.

The seminars are usually held every other Tuesday, from 1 PM to 2 PM, at the IST campus in Alameda. This sometimes changes due to availability of the speakers, so check regularly!

Tuesday, March 3rd 2020, 13h00 - 14h00

Guilherme Grijó Pires (Instituto de Telecomunicações/IST)

Variational Mixture of Normalizing Flows

Sala GA5, Pavilhão Central

Instituto Superior Técnico - Alameda

Abstract:

In the past few years, deep generative models, such as generative adversarial networks, variational autoencoders, and their variants, have seen wide adoption for the task of modelling complex data distributions. In spite of the outstanding sample quality achieved by those methods, they model the target distributions implicitly, in the sense that the probability density functions induced by them are not explicitly accessible. This fact renders those methods unfit for tasks that require, for example, scoring new instances of data with the learned distributions. Normalizing flows overcome this limitation by leveraging the change-of-variables formula for probability density functions, and by using transformations designed to have tractable and cheaply computable Jacobians. Although flexible, this framework lacked (until the publication of recent work) a way to introduce discrete structure (such as the one found in mixtures) in the models it allows to construct, in an unsupervised scenario. The present work overcomes this by using normalizing flows as components in a mixture model, and devising a training procedure for such a model. This procedure is based on variational inference, and uses a variational posterior parameterized by a neural network. As will become clear, this model naturally lends itself to (multimodal) density estimation, semi-supervised learning, and clustering.

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Bio: Guilherme is a ML Engineer / Data Scientist, and recent graduate from IST's MSc program in Electrical and Computer Engineering. Having worked at Jungle (https://jungle.ai), he developed an interest for the application of machine learning and data-driven techniques to the modelling of dynamical systems, such as power transformers, wind turbines, industrial components. Presently a consultant with DareData (https://daredata.engineering/), he has had the chance to work on a broad set of data-problems. He is currently trying to kickstart an academic research career, and he is interested in a probabilistic view of machine learning, generative models, and more recently in dynamical systems and causality.

Eventbrite - Variational Mixture of Normalizing Flows