CMIC deep learning journal club

The aim of this journal club is to discuss any subject related to deep learning and machine learning. This is an informal event with the aim of promoting discussions about the state of the art and other topics.

There will be two speakers per session. Material to be presented can be based on an upcoming paper, an interesting blog-post, a new code library and so forth.

Please sign up to the mailing list with your UCL e-mail at: http://www.mailinglists.ucl.ac.uk/mailman/listinfo/cmic-deeplearning-journalclub

  • Meetings are on the 3rd Monday of every month from 4-5pm unless specified.
Date Discussion leaders Paper/Topic Location
21/08/2017 4-5pm Jorge Cardoso
Felix Bragman
YOLO9000: Bester, Faster, Stronger
Overcoming catastrophic forgetting in neural networks
Engineering Front Building Executive Suite 1.03
18/09/2017 2-3pm Lorenz Berger
Mark Graham
Learning from Simulated and Unsupervised Images through Adversarial Training
Synthetic Medical Images from Dual Generative Adversarial Networks
Engineering Front Building Executive Suite 1.03
16/10/2017 4-5pm Carole Sudre
Wenqi Li
What uncertainties do we need in Bayesian deep learning for computer vision?
Learning multiple visual domains with residual adapters
Malet Place Engineering Building 1.04
20/11/2017 4-5pm Zach Eaton-Rosen Concrete Problems in AI Safety Malet Place Engineering Building 1.04
18/12/2017 4-5pm Kerstin Klaser
Irme Groothuis
Multimodal MR synthesis via modality-invariant latent representation
Unsupervised domain adapation in brain lesion segmentation with adversarial networks
Foster Court 113
22/01/2017 4-5pm Stephen Morrell
Nikolas Pontikos
Deformable Convolutional Networks
Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images
Engineering Front Building Executive Suite 1.03
19/02/2017 No journal club
19/03/2017 Tom Varsavsky
Marta Ranzini
Deep Sets
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Gordon Square (16-18) 101
23/04/2017 David Owen
Guotai Wang
Tell me where to look: guided attention inference network
Learn to pay attention
Malet Place 1.04