Machine learning seminar series
Main content
In this seminar series, we discuss diverse topics related to machine learning research. In addition, members of the machine learning group and visitors regularly present their work. Each seminar lasts for roughly 45 minutes (including questions) and is directly followed by a quick lunch offered to all attendees to stimulate more informal discussions.
Anyone is welcome! From master students, industry participants, researchers, and others are invited. No need to register (not even for the lunch), and the location is accessible without access card.
If you wish to be included in the seminar mailing list to be informed of next seminars, please contact Natacha Galmiche (natacha.galmiche@uib.no) or Odin Hoff Gardå (odin.garda@uib.no).
If you are interested in presenting, please contact Natacha Galmiche (natacha.galmiche@uib.no) or Odin Hoff Gardå (odin.garda@uib.no).
Date and location
For the spring semester 2023, we aim to have seminars every other Thursday at 11am. Please check the seminars below for more detailed information about specific seminars.
The seminars take place at University of Bergen, Department of informatics (Thormøhlens gate 55), whenever possible in the Lille auditorium seminar room. The room will be confirmed for each seminar. Please check below.
Seminars
(16.05.23) Samia Touileb
(11.05.23) Juha Harviainen
(05.05.23) Prayag Tiwari
(24.04.23) Fabio Massimo Zennaro
(19.04.23) Tung Kieu
(14.04.23) Zheng Zhao
(30.03.23) Ricardo Guimarães: An Introduction to Knowledge Graph Embeddings
Speaker: Ricardo Guimarães
Abstract: Knowledge Graphs (KGs) are data structures that represent entities as nodes and the different relations between them as edges, usually directed and labelled. KGs have become vital tools in applications such as information retrieval, knowledge management, data integration, disambiguation, and recommendation systems. However, most important knowledge graphs are highly incomplete. The need for deriving more information from KGs despite the missing data motivated the development of different KG Embedding models in Machine Learning to predict the missing links in a KG. In this talk, we introduce the fundamentals of KG embeddings and give an overview of the existing models and current challenges.
Date & Time: 30.03.23, 11am
Location: Blåbær
(16.03.23) Pekka Parviainen: Bayesian networks in modern times
Speaker: Pekka Parviainen
Abstract: Bayesian networks are probabilistic graphical models that are used to represent joint probability distributions of several variables. They can be found under the hood in many machine learning methods. In this talk, I will give a short introduction to Bayesian networks and discuss some recent research directions in the field.
Date & Time: 16.03.23, 11am
Location: Lille auditorium
(02.03.23) Ketil Malde: Deep metric learning
Speaker: Ketil Malde
Abstract: Metric learning aims to learn a distance measure between data points. This distance can in turn be used for verification (do data points represent the same object?), recognition (does a data point represent an object in a database of known objects?) and for clustering and other types of data analysis. Here we visit contemporary methods for deep metric learning (including contrastive, non-contrastive, self-supervised, and variational) and look at some of their applications.
Date & Time: 02.03.23, 11am
Location: Lille auditorium
(07.02.23) Vinay Chakravarthi Gogineni: Personalized Federated Learning
Speaker: Vinay Chakravarthi Gogineni
Abstract: Federated learning (FL) is a distributed learning paradigm that enables geographically dispersed edge devices, often called clients, to learn a global shared model on their locally stored data without revealing it. Due to its ability to handle system and statistical heterogeneity, FL has received enormous attention. Despite its success, the traditional FL is not well suited to many practical applications such as those that involve the internet-of-things (IoT) or cyber-physical systems (CPS), where edge devices are semi-independent with device-specific dynamic behavior characteristics. A single universal model for device-specific tasks is neither reasonable nor realistic. Therefore, it is necessary to allow each device to learn and use a local, personalized model. In this talk, a brief overview of traditional federated learning, personalized federated learning and challenges associated with them will be presented.
(02.02.23) Ming-Chang Lee: Real-time lightweight anomaly detection approaches for open-ended time series
Speaker: Ming-Chang Lee
(26.10.22) Roman Khotyachuk: Dimensionality Reduction Methods for Numerical Partial Differential Equations (PDEs)
Speaker: Roman Khotyachuk
Abstract: In this talk, we will consider dealing with high-dimensional data from numerical PDEs. First, I will review some approaches to Dimensionality Reduction (DR) when solving PDEs numerically. The second part will be devoted to general DR methods with applications to PDEs. And finally, I will present some examples of DR from my PhD project.
(05.10.22) Nello Blaser: Open Problems in Topological Machine Learning
Speaker: Nello Blaser
Abstract: Topological methods have recently gotten more traction in the machine learning community and are actively applied and developed. In this talk I will first give a short primer on topological machine learning and showcase how topological methods can be used in machine learning. The main part of the talk will then be devoted to give my perspective on important open problems that need to be addressed to allow for more wide-spread use of topological methods in machine learning.
(21.09.22) Ramin Hasibi: Geometric Machine Learning and Applications in Biology and Computer Vision
Speaker: Ramin Hasibi
Abstract: In my talk, I will discuss machine learning and specifically, deep learning methods applicable on geometric datasets. In geometric deep learning for datasets such as graphs, sets, 3D shapes or point clouds, the underlying structure of the dataset is utilized in the deep learning methods to improve the performance of the machine learning framework. Furthermore, graphs do not obey a certain structure pattern and size constrains. Therefore, the methods investigated in this work should be invariant to the size, structure and order of the elements in the dataset. This presentation is based on our recent works in the field of biology as well as our collaboration with colleagues at Aalto University of Technology for applications in computer vision and robotics.
