Machine Learning Blog

Monthly Archives: March 2019

ML seminar, Wed 3 Apr, 2pm

News, Seminar.

Machine Learning seminar

When: Wed, 3 Apr 2019, 2pm
Where: A226, College Building

Who: Derek Doran,Wright State University.

Title: Mappers and Manifolds Matter!

Abstract: Topological Data Analysis (TDA) is a branch of data science that estimates and then exploits the “shape” of a dataset for downstream characterization and inference. TDA methods arerising in popularity in the ML community as a tool to theoretically understand the actions of deep neural nets and other algorithms by connections to the Manifold Hypothesis. TDA methods, and in particular the Mapper algorithm, is also finding recent increased use in applied data science workflows. This talk will introduce the essential definitions and notions from Topology needed for audience members to jump into the field of TDA and to build Mappers of datasets. It will then demonstrate the utility of Mapper, and TDA methods generally, for data science tasks including complex data visualization, interpretable dimensionality reduction, and explainable deep learning. The application of Mapper for dataset characterization and semi-supervised learning will also be illustrated.

Bio: Derek Doran is an Associate Professor of Computer Science and Engineering at Wright State University, Dayton OH, USA. His research interests are in machine learning and complex systems analysis, with current emphasis on topological methods for explainable AI, deep learning, complex network analysis, and web and geospatial systems mining. He is an author of over 75 publications on these topics, four of which have been recognized with best paper awards and nominations, is an author on multiple patents, and has published a book under the Springer Briefs in Complexity series. He serves on the program committee of major AI and Web conferences, is on the Editorial Board of Social Network Analysis and Mining and has served as process improvement chair at ESWC. Dr. Doran is a National Science Foundation EAPSI Fellow, a graduate research awardee of the U.S. Transportation Research Board, and a twice summer alumnus of Bell Labs. He will be a Fulbright Fellow stationed in Reykjavik University effective January 2020. Please see more information at https://derk–

All welcome!

ML seminar, Wed 13 Mar, 2pm

News, Seminar.

Machine Learning seminar

When: Wed, 13 Mar 2019, 2pm
Where: A226, College Building

Who: Robin Manhaeve, Katholieke Universiteit Leuven, Belgium.

Title: DeepProbLog: Neural Probabilistic Logic Programming

Abstract: We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques can be adapted for the new language. Our experiments demonstrate that DeepProbLog supports both symbolic and subsymbolic representations and inference, 1) program induction, 2) probabilistic (logic) programming, and 3) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.

Bio: Robin Manhaeve is a PhD student at the department of Computer Science at the KU Leuven. In 2017, he completed his MSc in Engineering Science: Computer Science at the KU Leuven. He’s currently researching the  integration of Deep Learning and Probabilistic Logic Programming under the supervision of Prof. Luc De Raedt and is funded by an SB grant from the Research Foundation – Flanders (FWO).

All welcome!

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