Deep Learning and Reinforcement Learning is a thriving field of science. Properly managed, such algorithms can obtain great results. However, there are many pitfalls, that many of us have bumped into and overcome individually.
In order to streamline this process and possibly avoid such pitfalls we are organizing joint sessions between machine learning enthusiasts in Delft and Leiden, to exchange experiences and knowledge. The goal is to discuss low level practicalities that arose while working with Neural Networks and machine learning methods more generally.
We are aiming to have a meeting every 2 months, alternating between Delft and Leiden. These are announced over a dedicated mailing list, please contact Rolf Starre if you want to be added to this.
Jan van Rijn (main contact Leiden)
Rolf Starre (main contact Delft)
19 September 2019
The program will be as follows:
10:00 – 12:00 technical session:
- presentation “Asking questions about Deep Learning Research” by Jan van Gemert,
- presentation “Scale selective structured filters: How to learn scale in a deep network?” by Silvia Pintea.
12:00 – 13:00 informal lunch together (not provided)
Title: Asking questions about Deep Learning Research
By Jan van Gemert (TU Delft)
Abstract: How to go about doing research in deep learning? Datasets are massive,
the computational resources have huge requirements but are only
scarcely available, the downloaded code does not compile, and other
‘details’ should not prevent doing ‘good’ scientific research. What is
good research anyway? And how to communicate the findings to the rest of
Title: Scale selective structured filters: How to learn scale in a deep network?
By Silvia Pintea (TU Delft)
Abstract: I will be presenting my ongoing work about automatically learning scale in deep networks.
The proposed idea is based on scale-space theory and uses Gaussian derivatives to define a basis for constructing network filters.
There are challenges to overcome in terms of memory/computational demands of this new model,
as well as in terms of identifying a scenario in which the proposed method has added value over existing pixelwise filter learning methods.
Snellius Building, Room 413
Niels Bohrweg 1, 2333 CA Leiden