Machine Learning Coffee Seminars2020-01-13T12:52:12+02:00

About Us

Machine Learning Coffee seminars are weekly seminars organized by Finnish Center for Artificial Intelligence (FCAI) and are 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.

Please see FCAI’s YouTube channel for video recordings of past talks.

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

Upcoming Talks (2020)

January 2020

Quality of Analytics as an Approach for Optimizing ML Systems: Initial Results and Roadmap

Date: January 27, 2020

Abstract: Machine learning (ML) systems need to be optimized in an end-to-end system manner, not just for ML algorithms and models. Therefore, recently, the role of software systems and underlying distributed computing platforms and their intersections with ML has been discussed intensively in systems and ML communities. Various abilities of ML systems, such as robustness, reliability and responsiveness, rely on the capabilities of underlying computing and data platforms, and optimizing ML systems requires a strong integration with software and systems engineering techniques. In our work, we are interested in addressing challenging runtime issues in the robustness, reliability, resilience and elasticity (R3E) of end-to-end ML systems. In this talk, we will discuss the view of quality of analytics (QoA) and the principles of elasticity engineering for big data and cloud computing that can be applied to ML systems. We will present initial results of applying QoA for ML pipelines and discuss a long-term view research activities to support QoA for runtime optimization of ML systems.

Speaker: Hong-Linh Truong

Affiliation: Associate professor of Computer Science Departments, AaLto University

Place of Seminar: Lecture Hall T6, Aalto University

February 2020

ODE2VAE: Deep generative second-order ODEs with Bayesian neural networks

Date: February 10, 2020

Abstract: Recently, there has been a growing interest in solving ordinary differential equation (ODE) systems using function approximators, e.g., Gaussian processes or neural networks. Such models are proven useful for modeling continuous-time dynamic phenomena and also interestingly connected with very deep networks with skip connection, flow-based deep generative models and reinforcement learning.
In this talk, I’ll present an overview of our NeurIPS paper from last December: ODE2VAE, a latent second-order ODE model for high-dimensional sequential data. ODE2VAE can simultaneously learn the embedding of high dimensional trajectories and infer arbitrarily complex continuous-time latent dynamics. In particular, the talk will focus on the advantages of ODE modeling over discrete counterparts, why latent modeling is useful and the benefits of being Bayesian in this setting. To complete the picture, I’ll briefly give the historical context, draw connections with related techniques, and discuss exciting future directions.

Speaker: Yildiz Cagatay

Affiliation: Doctoral Candidate at Aalto University, Department of Computer Science

Place of Seminar: Lecture Hall T6, Konemiehentie 2, Aalto University

Intelligent service assistant for people in Finland

Date: February 17, 2020

Speaker: Tommi Mikkonen & Aleksi Kopponen
Affiliation: Professor of Computer Science, Helsinki University & Special Advisor of Digitalization, Minister of Finland

Place of Seminar: Lecture Hall Exactum D122, University of Helsinki

April 2020

AI-driven Health

Date: April 06, 2020

Speaker: Pekka Marttinen

Affiliation: Assistant Professor (Tenure track) in Machine Learning, Aalto University

Place of Seminar: Lecture Hall T5, Konemiehentie 2, Aalto University


Samuel Kaski Professor of Computer Science, Aalto University
Laura Ruotsalainen Professor of Computer Science, University of Helsinki
Khaoula El Mekkaoui Doctoral Student, Aalto University