Machine Learning Coffee Seminars

Machine Learning Coffee Seminars2018-08-31T07:02:49+00:00

About Us

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Seminars will be held weekly on Mondays at 9 am – 10 am. The location alternates between Aalto University and the University of Helsinki. At Aalto University, talks will be held in Konemiehentie 2, seminar room T6 and at the University of Helsinki in Kumpula, seminar room Exactum D122 (Gustaf Hällströmin katu 2b), unless otherwise noted. Talks will begin at 9:15 am and coffee will be served from 9:00 am.

Subscribe to our mailing list where seminar topics are announced beforehand.

Upcoming Talks (2018)

October 2018

Human-guided data exploration

Date: October 22, 2018

Abstract: The outcome of the explorative data analysis (EDA) phase is vital for successful data analysis. EDA is more effective when the user interacts with the system used to carry out the exploration. A good EDA system has three requirements: (i) it must be able to model the information already known by the user and the information learned by the user, (ii) the user must be able to formulate the objectives, and (iii) the system must be able to show the user views that are maximally informative about desired features data that are not already know for the user. Furthermore, the system should be fast if used in interactive system. We present the Human Guided Data Exploration framework which satisfies these requirements and generalizes previous research,. This framework allows the user to incorporate existing knowledge into the exploration process, focus on exploring a subset of the data, and compare different complex hypotheses concerning relations in the data. The framework utilises a computationally efficient constrained randomization scheme. To showcase the framework, we developed a free open-source tool, using which the empirical evaluation on real-world data sets was carried out. Our evaluation shows that the ability to focus on particular subsets and being able to compare hypotheses are important additions to the interactive iterative data mining process.

For references see:
Puolamäki Kang, Lijffijt, De Bie,  2016, Interactive Visual Data Exploration with Subjective Feedback. In Proc ECML PKDD,
Puolamäki, Oikarinen, Kang, Lijffijt, De Bie, 2018. Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach. In Proc ICDE 2018,
Puolamäki, Oikarinen, Atli, Henelius, 2018. Human-guided data exploration using randomization.

Speaker: Kai Puolamäki

Affiliation: Professor of Computer Science, University of Helsinki

Place of Seminar: University of Helsinki

Noise2Noise: Learning Image Restoration without Clean Data

Date: October 29, 2018

Abstract: We apply basic statistical reasoning to signal reconstruction by machine learning–learning to map corrupted observations to clean signals–with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data. These results have significant implications for ease of training high-performance image restoration models and certain inverse problem solvers.

Joint work with Jacob Munkberg, Jon Hasselgren, Timo Aila, Tero Karras, Samuli Laine, and Miika Aittala.

Speaker: Jaakko Lehtinen

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: Seminar Room T5, Konemiehentie 2, Aalto University


Samuel Kaski  Professor of Computer Science, Aalto University
Teemu Roos Professor of Computer Science, University of Helsinki
Homayun Afrabandpey Researcher, Aalto University