Fridays Journal Club



Jun 30th, 15:00-17:00, C3.163, Deep and Hierarchical Implicit Models, ChangYong Oh

Jun 23th, 15:00-17:00, C3.163, Reinforced Variational Inference, Karen Ullrich

Jun 9th, 15:00-17:00, C3.163, Reinforcement Learning with Unsupervised Auxiliary Tasks, Elise van der Pol

Mar 17th, 15:00-17:00, C3.163, The More You Know: Using Knowledge Graphs for Image Classification, Thomas Kipf

Mar 10th, 16:00-17:00, C3.163, Hopfield Networks, Karen Ullrich

Mar 3rd, 15:00-17:00, C3.163, Understanding Deep Learning requires rethinking generalization, ChangYong Oh

Feb 17th, 15:00-17:00, C3.163, Why does deep and cheap learning work so well?, Rajat Thomas

Feb 3rd, 15:00-17:00, C3.163, A Neural Autoregressive Approach to Collaborative Filtering, Rianne van de Berg



Dec 23rd, Canceled, –, –, Canceled

Dec 16th, 15:00-17:00, C3.163, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering & Tutorial on good coding practices, Thomas Kipf & Peter O’Connor

Dec 9th, Canceled, –, –, Canceled

Dec 2nd, 15:00-17:00, C3.163, TBA & Adversarial Autoencoders, Mijung Park & Matthias Reisser

Nov 25th, 15:00-17:00, C3.163, Rényi Divergence Variational Inference & Operator Variational Inference, Jakub Tomczak & Christos Louizos

Nov 18th, 15:00-17:00, C3.163, TBA & ML solutions for Cryo-EM, Ted Meeds & Karen Ullrich

Nov 11th, 15:00-17:00, C3.163, Influence maximization in stochastic and adversarial settings, Po-Ling Loh (University of Wisconsin)

Nov 4th, Canceled, –, –, Canceled

Oct 28th, 15:00-17:00, C3.163, Equilibrium Propagation & Exponential expressivity in deep neural networks through transient chaos, Max Losch & ChangYong Oh

Oct 21st, 15:00-17:00, C3.163, Semantic Parsing with Semi-Supervised Sequential Autoencoders, Diego Mareggiani & Peter O’Connor

Oct 14th, 15:00-17:00, C3.163, Constructing Summary Statistics for Approximate Bayesian Computation: Semi-automatic ABC & Towards Conceptual Compression, Tameem Adel & Patrick Putzky

Sep 30th, 15:00-17:00, C3.163, Review of: 2016 Workshop on Human Interpretability in Machine Learning & TBA, Mijung Park & Rajat Thomas

Sep 23rd, 15:00-16:00, C3.163, Review of some recent ML datasets and open problems, Karen Ullrich

Sep 16th, 15:00-17:00, C3.163, Decoupled Neural Interfaces Using Synthetic Gradients & Discrete Variational Autoencoders, Matthias Reisser & Christos Louizos

Jul 15th, 15:00-17:00, C3.163, TBA, Peter O’Connor & Sergii Gavrylov

Jul 8th, 15:00-17:00, C3.163, From Probabilistic Models to Decision Theory and Back Again, Sanmi (Oluwasanmi) Koyejo

Jul 1st, 15:00-17:00, C3.163, Learning to Learn by Gradient Descent by Gradient Descent & TBA, ChangYong Oh & Karen Ullrich

Jun 24th, 15:00-17:00, C3.163, Learning CNNs for Graphs & Towards a Neural Statistician, Thomas Kipf & Max Losch

Jun 17th, 15:00-17:00, C3.163, Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models, Misha Chertkov

Jun 3rd, 15:00-17:00, C3.163, TBA & Black-Box Alpha-Divergence Minimisation, Taco & Matthias

Jun 3rd, 15:00-17:00, C3.163, Programs as Probabilistic Models, Brooks Paige