Every month at Algorithm Club, our postgraduate students pick some machine-learning model to study and discuss.
- Any one of these models: https://adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html
- The AlphaFold model that famously predicts protein folding: https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery
- Spatial transformer networks, suggested by Maj.
- Explanability in deep-learning algorithms, suggested by Stavros.
Date |
Time |
Topic |
|---|---|---|
| 4th November, 2021 | 09:30–10:30 | INVASE by Fergus Pick. |
| 21st October, 2021 | 09:30–10:30 | Intellectual property protection of neural networks by Hanchi Ren; and IP protection of neural networks using watermarking by Majedaldein Almahasneh. |
| 7th October, 2021 | 09:30–10:30 | Explaining the machine learning model predictions by Stavros Georgousis. |
| 24th June, 2021 | 09:30–10:30 | Spatial transformer networks (paper, Towards Data Science "review"), presented by Majedaldein Almahasneh (slides). |
| 27th May, 2021 | 09:30–10:30 | Temporal convolutional networks, presented by Suraj Ramchand (slides). |
| 29th April, 2021 | 09:30–10:30 | AlphaFold (the AlphaFold paper and the description of AlphaFold's entry into CASP13 and an abstract of AlphaFold2 for CASP14 (p. 22)), presented by Chen Hu (slides). |
| 1st April, 2021 | 09:30–10:30 | Graph convolution on graphs: advanced topics, presented by Stavros Georgousis and Michael Kenning (part 1, part 2). |
| 4th March, 2021 | 09:30–10:30 | Graph convolution on graphs: the basics, presented by Stavros Georgousis and Michael Kenning (part 1, part 2). See the survey of the state of the art of graph deep learning and its challenges. |
| 4th February, 2021 | 09:30–10:30 | A discussion of AlexNet and the subsequent analysis of it conducted by Zeiler and Fergus. The papers were presented by Alex Milne (slides). |