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