Machine Learning
Facilitating research collaborations between divisions and between the department of Information technology and external parts.
Machine Learning Journal club
The arena organizes a journal club focused on topics in machine learning that are of interest to researchers across the divisions of the IT-department. The goal is to disseminate knowledge and encourage research collaborations. The most recent journal club was held in the format of a seminar course.
Spring 2022: High-dimensional statistical models
TBA
Fall 2021: Overparameterized Machine Learning Models
Given as a seminar course.
Fall 2019: Adversarial Machine Learning
Seminar #1: Introducing adversarial examples
- Paper
- Presenters: Dave Zachariah
- Date: Oct 10th, 10.15
- Location: ITC, 4306 (next to fika rooms)
Seminar #2: Generating adversarial examples
- Paper
- Presenters: Dave Zachariah
- Date: Oct 17th, 10.15
- Location: ITC, 4306 (next to fika rooms)
Seminar #3: Adversarial learning and robustness
- Paper
- Presenters: Dave Zachariah
- Date: Nov 7th, 10.15
- Location: ITC, 4306 (next to fika rooms)
Previous
Spring 2018: Learning Complex Models via Approximate Inference
Seminar #1: Variational Inference: An overview
- Paper: Variational Inference: A Review for Statisticians
- Presenters: Christian A. Naesseth
- Date: Feb 19th, 15.00 - 16.30
- Location: ITC, 4306 (next to fika rooms)
Seminar #2: Black Box Variational Inference
- Paper: Black Box Variational Inference
- Presenters: Dave Zachariah
- Date: March 26th, 13.15-14.30
- Location: ITC, 4306
Seminar #3: Deep Generative Models
- Paper: Stochastic Backpropagation and Approximate Inference in Deep Generative Models
- Presenters: Carl Andersson
- Date: April 16th, 13.15-14.30
- Location: ITC, 4306
- Presentation: Download
Spring 2017: Introduction to Machine Learning
Seminar #1: Gaussian Processes
- Paper: Gaussian Processes for Big Data
- Presenters: Fredrik Wahlberg and Andreas Svensson
- Date: Feb 15th, 13.00 - 15.00
- Location: ITC, 4308 (common fika room)
Seminar #2: Probabilistic Programming
- Papers: (introductory) A Compilation Target for Probabilistic Programming Languages and (advanced) Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation
- Presenters: Lawrence Murray and Jan Kudlicka
- Date: Mar 29th, 13.00 - 15.00
- Location: ITC, 4308 (common fika room)
- Slides: Download
Seminar #3: Deep Learning
- Papers: (introductory) Deep Learning and (applied, CNN) Dermatologist-level classification of skin cancer with deep neural networks (applied, RNN) The Unreasonable Effectiveness of Recurrent Neural Networks
- Presenters: Niklas Wahlström, Carl Andersson and Tomas Wilkinson
- Date: Apr 12th, 13.00 - 15.00
- Location: ITC, 4308 (common fika room)
- Slides: Download
Seminar #4: Bayesian Optimization
- Papers: Taking the Human Out of the Loop: A Review of Bayesian Optimization
- Presenters: Prashant Singh
- Date: Apr 26th, 14.00 - 15.00
- Location: ITC, 4308 (common fika room)