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 mailing list 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, April 17th 2018, 13h00 - 14h00

Volodymyr Miz (EPFL)

Wikipedia Graph Mining: Dynamic structure of collective memory

PA2

Instituto Superior Técnico - Alameda

Abstract:

Wikipedia is the biggest encyclopedia ever created and the fifth most visited website in the world. Tens of millions of people surf it every day, seeking answers to various questions. Collective user activity on its pages leaves publicly available footprints of human behavior, making Wikipedia an excellent source for analysis of collective behavior. Collective memory is an interesting social phenomenon of human behavior. Studying this concept is a way to enhance our understanding of a common view of events in social groups and identify the events that influence collective remembering of the past. Collective memory hypothesis influenced a range of studies in sociology, psychology, cognitive sciences, and, only recently, in machine learning. We applied a data mining approach to studying collective memories.

In this talk, I will demonstrate a new method for collective memory retrieval and show how interests of Wikipedia visitors evolve over time. To reveal memory patterns, we analyze the seven months logs of user activity on Wikipedia and its Web network structure (5K+ hours, 100K+ active pages, 6.5M+ links). We use the Hopfield network model as an artificial memory abstraction to build a macroscopic collective memory model. Each pattern in the Hopfield network is a cluster of Wikipedia pages sharing a common topic and describing an event that triggered human curiosity during a finite period of time.

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Bio: Volodymyr Miz is an Electrical Engineering PhD student at the EPFL (Swiss Federal Institute of Technology). His interests are related to graphs and networks analysis, time-series processing and data mining. His research focuses primarily on time-varying data with an underlying network structure. After obtaining BS and MS degrees in Computer Engineering from National University of Radio Electronics (Kharkov, Ukraine) in 2013, he worked for four years as a software engineer in a telecommunication company EchoStar and then joined EPFL as a PhD student.

Eventbrite - Wikipedia Graph Mining: Dynamic structure of collective memory