Past Papers

Here’s a list of the papers/videos that have previously been presented.

Date	Presented By	Paper Title
3rd December	Pierre-Aurelien Gilliot	Deep Boltzmann Machines - Ruslan Salakhutdinov
10th December	Christophe Andrieu	Multiscale Models for Image Classification and Physics with Deep Networks - Stéphane Mallat
17th December	Anthony Lee	Nonparametric regression using deep neural networks with ReLU activation function - Johannes Schmidt-Hieber
10th March	Patrick Rubin-Delanchy	Uniform convergence may be unable to explain generalization in deep learning - Vaishnavh Nagarajan
24th March	Song Liu	Neural Ordinary Differential Equations - Ricky T. Q. Chen
7th April	Mark Beaumont	Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation - Samuel Wiqvist
14th April	Mauro Camara Escudero	Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights - Daniel Soudry
21st April	Mingxuan Yi	Opening the black box of Deep Neural Networks via Information - Ravid Schwartz-Ziv
28th April	Andi Wang	Markov Chain Monte Carlo and Variational Inference: Bridging the Gap - Tim Salimans
5th May	Sam Tickle	Deep Learning for Multi-Scale Changepoint Detection in Multivariate Time Series - Zahra Ebrahimzadeh
26th May	Pierre Aurelien Gilliot	Preventing Posterior Collapse with delta-VAEs - Ali Razavi
2nd June	Christophe Andrieu	MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference - Achille Thin
9nd June	Anthony Lee	From optimal transport to generative modeling: the VEGAN cookbook - Olivier Bousquet
25th June	Song Liu	Stein Variational Gradient Descent as Gradient Flow - Qiang Liu
30th June	Mark Beaumont	Mining gold from implicit models to improve likelihood-free inference - Johann Brehmer et al.
7th July	Mauro Camara Escudero	Auto-Encoding Variational Bayes - Diederik P. Kingma
14th July	Mingxuan Yi	Neural Processes - Marta Garnelo
28th July	Mauro Camara Escudero	Variational Inference with Normalizing Flows - Rezende & Mohamed
19th October	Chang Zhang	Learning in Implicit Generative Models - Mohamed et al
28th October	Mauro Camara Escudero	Adversarial Variational Bayes - Mescheder et al.
2nd November	Song Liu	Likelihood-free MCMC with Amortized Approximate Ratio Estimators - Hermans et al.
9nd November	Pierre Aurelien Gilliot	Learning Latent Subspaces in Variational Autoencoders - Klys et al.
16th November	Mark Beaumont	Analyzing Inverse Problems with Invertible Neural Networks - Ardizzone et al.
23rd November	Mingxuan Yi	Neural Tangent Kernel: Convergence and Generalization in Neural Networks - Jacot et al.
30th November	Christophe Andrieu	Estimation under invariant distributions - Yamato et al.
7th December	Andi Wang	Toward a theory of optimization for over-parameterized systems of non-linear equations: the lessons of deep learning - Liu et al.
14th December	Anthony Lee	Stochastic Normalizing Flows - Wu et al.
3rd February	Sam Tickle	Deep learning of contagion dynamics on complex networks - Murphy et al.
10rd February	Patrick Rubin-Delanchy	Neural Word Embedding as Implicit Matrix Factorization - Levy et al.
24th February	Sam Power	How to Train Your Energy-Based Models - Song et al.
3rd March	Henry Reeve	NeuralFDR: Learning Discovery Thresholds from Hypothesis Features - Xia et al.
31st March	Mark Beaumont	Sequential Neural Posterior and Likelihood Approximation - Wiqvist et al.
7th April	Anthony Lee	A Shooting Formulation of Deep Learning - Vialard et al.
14th April	Chang Zhang	The Thermodynamic Variational Objective - Masrani et al.
21st April	Song Liu	Telescoping Density-Ratio Estimation - Rhodes et al.
29th April	Mauro Camara Escudero	Differentiable Particle Filtering via Entropy-Regularized Optimal Transport - Corenflos et al.
6th May	Mingxuan Yi	Generalized Sliced Wasserstein Distances - Kolouri et al.
12th May	Andi Wang	Hopfield Networks is All You Need - Ramsauer et al.
9th June	Henry Reeve	Test-Time Training with Self-Supervision for Generalization under Distribution Shifts - Sun et al.
23rd June	Sam Power	Deep Gaussian Processes - Damianou et al.
30rd June	Daniel Lawson	A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks - Chan et al.