Machine Learning Coffee Seminars

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)

Give a talk

If you are interested in giving a talk, feel free to send your suggested topic and abstract to mlseminar@hiit.fi

Spring 2018


April 23

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

Abstract: For the study of molecules and materials, conventional theoretical and experimental spectroscopies are well established in the natural sciences, but they are slow and expensive. Our objective is to launch a new era of artificial intelligence (AI) enhanced spectroscopy that learns from the plethora of already available experimental and theoretical spectroscopy data. Once trained, the AI can make predictions of spectra instantly and at no further cost. In this new paradigm, AI spectroscopy would complement conventional theoretical and experimental spectroscopy to greatly accelerate the spectroscopic analysis of materials, make predictions for novel and hitherto uncharacterized materials, and discover entirely new materials.

In this presentation, I will introduce the two approaches we have used to learn spectroscopic properties: kernel ridge regression (KRR) and deep neural networks (NN). The models are trained and validated on data generated by accurate state-of-the art quantum chemistry computations for diverse subsets of the GBD-13 and GBD-17 molecular datasets [1,2]. The molecules are represented by a simple, easily attainable numerical description based on nuclear charges and cartesian coordinates [3,4]. The complexity of the molecular descriptor [4] turns out to be crucial for the learning success, as I will demonstrate for KRR. I will then show, how we can learn spectra (i.e. continuous target quantities) with NNs. We design and test three different NN architectures: multilayer perceptron (MLP) [5], convolutional neural network (CNN) and deep tensor neural network (DTNN) [6]. Already the MLP is able to learn spectra, but the  learning quality improves significantly for the CNN and reaches its best performance for the DTNN. Both CNN and DTNN capture even small nuances in the spectral shape.

* This work was performed in collaboration with A. Stuke, K. Ghosh,  L. Himanen, M. Todorovic, and A. Vehtari

[1] L. C. Blum et al., J. Am. Chem. Soc. 131, 8732 (2009)
[2] R. Ramakrishnan et al., Scientific Data 1, 140022 (2014)
[3] M. Rupp et al., Phys. Rev. Lett. 108, 058301 (2012)
[4] H. Huo and M. Rupp, arXiv:1704.06439
[5] G. Montavon et al., New J. Phys. 15, 095003 (2013)
[6] K. T. Schutt et al., Nat. Comm. 8, 13890 (2017)

Speaker: Patrick Rinke

Affiliation: Professor of Physics, Aalto University

Place of Seminar: University of Helsinki

May 7

Abstract:

Speaker:

Affiliation:

Place of Seminar: University of Helsinki

May 14

Minisymposium on Interactive AI

Organizers: Giulio Jacucci  and Antti Oulasvirta

Talks

Computational Rationality: Convergence of Machine Learning, Neuroscience, Cognitive Science, Robotics, and HCI

Abstract: TBD.

Speaker: Antti Oulasvirta

Interactive Intent Modelling and Knowledge Elicitation

Abstract: TBD.

Speaker: Samuel Kaski

Interactive Robot Learning

Abstract: TBD.

Speaker: Ville Kyrki

Flash Talks

 

Place of Seminar: Aalto University


May 21

Abstract: tba

Speaker: Harri Lähdesmäki

Affiliation: Aalto University

Place of Seminar: University of Helsinki

May 28

Minisymposium on Privacy-Preserving and Secure AI

Organizers: N. Asokan  and Antti Honkela

Talks

Speaker: N. Asokan
Speaker: Antti Honkela

Flash Talks

 

Place of Seminar: Aalto University

 

History of Previous Talks

 

Organizers


Samuel Kaski  Professor of Computer Science, Aalto University
Teemu Roos Associate Professor of Computer Science, University of Helsinki
Homayun Afrabandpey PhD Student, Aalto University

 


Last updated on 22 Apr 2018 by Teemu Roos - Page created on 3 Dec 2016 by Homayun Afrabandpey