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