Publications (96)
HexaConv
Emiel Hoogeboom, Jorn W. T. Peters, Taco S. Cohen +1
Improved Bayesian Compression
Marco Federici, Karen Ullrich, Max Welling
Herding as a Learning System with Edge-of-Chaos Dynamics
Yutian Chen, Max Welling
POPE: Post Optimization Posterior Evaluation of Likelihood Free Models
Edward Meeds, Michael Chiang, Mary Lee +3
The Variational Fair Autoencoder
Christos Louizos, Kevin Swersky, Yujia Li +2
Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification
Jakub M. Tomczak, Maximilian Ilse, Max Welling
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
Edward Meeds, Max Welling
Variational Dropout and the Local Reparameterization Trick
Diederik P. Kingma, Tim Salimans, Max Welling
On Smoothing and Inference for Topic Models
Arthur Asuncion, Max Welling, Padhraic Smyth +1
On the Choice of Regions for Generalized Belief Propagation
Max Welling
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior
Yutian Chen, Max Welling
Learning Sparse Neural Networks through $L_0$ Regularization
Christos Louizos, Max Welling, Diederik P. Kingma
Sigma Delta Quantized Networks
Peter O'Connor, Max Welling
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
Anoop Korattikara, Yutian Chen, Max Welling
Bayesian Random Fields: The Bethe-Laplace Approximation
Max Welling, Sridevi Parise
Hybrid Variational/Gibbs Collapsed Inference in Topic Models
Max Welling, Yee Whye Teh, Hilbert Kappen
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior
Yutian Chen, Max Welling
The Deep Weight Prior
Andrei Atanov, Arsenii Ashukha, Kirill Struminsky +2
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
Christos Louizos, Max Welling
Bayesian Dark Knowledge
Anoop Korattikara, Vivek Rathod, Kevin Murphy +1
Neural Relational Inference for Interacting Systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang +2
Modeling Relational Data with Graph Convolutional Networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem +3
Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC
Sungjin Ahn, Anoop Korattikara, Nathan Liu +2
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Tim Salimans, Diederik P. Kingma, Max Welling
Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation
Ian Porteous, Alexander T. Ihler, Padhraic Smyth +1
MLitB: Machine Learning in the Browser
Edward Meeds, Remco Hendriks, Said Al Faraby +2
Variational Graph Auto-Encoders
Thomas N. Kipf, Max Welling
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
Christos Louizos, Max Welling
Spherical CNNs
Taco S. Cohen, Mario Geiger, Jonas Koehler +1
Structured Region Graphs: Morphing EP into GBP
Max Welling, Thomas P. Minka, Yee Whye Teh
Gravity in 2+1 dimensions as a Riemann-Hilbert problem
Max Welling
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets
Diederik P. Kingma, Max Welling
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
Luisa M Zintgraf, Taco S Cohen, Tameem Adel +1
Some approaches to 2+1-dimensional gravity coupled to point-particles
Max Welling
Improving Variational Inference with Inverse Autoregressive Flow
Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz +3
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Maurice Weiler, Mario Geiger, Max Welling +2
Primal-Dual Wasserstein GAN
Mevlana Gemici, Zeynep Akata, Max Welling
Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos, Uri Shalit, Joris Mooij +3
Variational Bayes In Private Settings (VIPS)
Mijung Park, James Foulds, Kamalika Chaudhuri +1
A New Method to Visualize Deep Neural Networks
Luisa M. Zintgraf, Taco S. Cohen, Max Welling
Super-Samples from Kernel Herding
Yutian Chen, Max Welling, Alex Smola
Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow
Jakub M. Tomczak, Max Welling
Improving Variational Auto-Encoders using Householder Flow
Jakub M. Tomczak, Max Welling
Extraction of Airways using Graph Neural Networks
Raghavendra Selvan, Thomas Kipf, Max Welling +3
Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks
Jasper Linmans, Jim Winkens, Bastiaan S. Veeling +2
Generalized Belief Propagation on Tree Robust Structured Region Graphs
Andrew E. Gelfand, Max Welling
Rotation Equivariant CNNs for Digital Pathology
Bastiaan S. Veeling, Jasper Linmans, Jim Winkens +2
Mean Field Network based Graph Refinement with application to Airway Tree Extraction
Raghavendra Selvan, Max Welling, Jesper H. Pedersen +2
Soft Weight-Sharing for Neural Network Compression
Karen Ullrich, Edward Meeds, Max Welling
Sinkhorn AutoEncoders
Giorgio Patrini, Rianne van den Berg, Patrick Forré +5
VAE with a VampPrior
Jakub M. Tomczak, Max Welling
Automatic Variational ABC
Alexander Moreno, Tameem Adel, Edward Meeds +2
Deep Scale-spaces: Equivariance Over Scale
Daniel E. Worrall, Max Welling
Quantum Mechanics of a Point Particle in 2+1 Dimensional Gravity
Hans-Juergen Matschull, Max Welling
Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf, Max Welling
Predictive Uncertainty through Quantization
Bastiaan S. Veeling, Rianne van den Berg, Max Welling
Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation
Max Welling, Yee Whye Teh
Attention-based Deep Multiple Instance Learning
Maximilian Ilse, Jakub M. Tomczak, Max Welling
Group Equivariant Convolutional Networks
Taco S. Cohen, Max Welling
Efficient Parametric Projection Pursuit Density Estimation
Max Welling, Richard S. Zemel, Geoffrey E. Hinton
A note on privacy preserving iteratively reweighted least squares
Mijung Park, Max Welling
Herding Dynamic Weights for Partially Observed Random Field Models
Max Welling
Sylvester Normalizing Flows for Variational Inference
Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak +1
Bayesian Compression for Deep Learning
Christos Louizos, Karen Ullrich, Max Welling
Scalable MCMC for Mixed Membership Stochastic Blockmodels
Wenzhe Li, Sungjin Ahn, Max Welling
Differentiable probabilistic models of scientific imaging with the Fourier slice theorem
Karen Ullrich, Rianne van den Berg, Marcus Brubaker +2
Steerable CNNs
Taco S. Cohen, Max Welling
Graph Convolutional Matrix Completion
Rianne van den Berg, Thomas N. Kipf, Max Welling
Transformation Properties of Learned Visual Representations
Taco S. Cohen, Max Welling
Relaxed Quantization for Discretized Neural Networks
Christos Louizos, Matthias Reisser, Tijmen Blankevoort +2
Deep Spiking Networks
Peter O'Connor, Max Welling
Gauge Equivariant Convolutional Networks and the Icosahedral CNN
Taco S. Cohen, Maurice Weiler, Berkay Kicanaoglu +1
Harmonic Exponential Families on Manifolds
Taco S. Cohen, Max Welling
Recurrent Inference Machines for Solving Inverse Problems
Patrick Putzky, Max Welling
Emerging Convolutions for Generative Normalizing Flows
Emiel Hoogeboom, Rianne van den Berg, Max Welling
Accelerating the BSM interpretation of LHC data with machine learning
Gianfranco Bertone, Marc Peter Deisenroth, Jong Soo Kim +3
Semisupervised Classifier Evaluation and Recalibration
Peter Welinder, Max Welling, Pietro Perona
Attention, Learn to Solve Routing Problems!
Wouter Kool, Herke van Hoof, Max Welling
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
Sungjin Ahn, Anoop Korattikara, Max Welling
Probabilistic Binary Neural Networks
Jorn W. T. Peters, Max Welling
Hamiltonian ABC
Edward Meeds, Robert Leenders, Max Welling
Convolutional Networks for Spherical Signals
Taco Cohen, Mario Geiger, Jonas Köhler +1
Private Topic Modeling
Mijung Park, James Foulds, Kamalika Chaudhuri +1
Temporally Efficient Deep Learning with Spikes
Peter O'Connor, Efstratios Gavves, Max Welling
Semi-Supervised Learning with Deep Generative Models
Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed +1
DP-EM: Differentially Private Expectation Maximization
Mijung Park, Jimmy Foulds, Kamalika Chaudhuri +1
Exploiting the Statistics of Learning and Inference
Max Welling
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
Edward Meeds, Max Welling
Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation
James Foulds, Levi Boyles, Christopher Dubois +2
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
Wouter Kool, Herke van Hoof, Max Welling
Herded Gibbs Sampling
Luke Bornn, Yutian Chen, Nando de Freitas +3
Covariance in Physics and Convolutional Neural Networks
Miranda C. N. Cheng, Vassilis Anagiannis, Maurice Weiler +3
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis
James Foulds, Joseph Geumlek, Max Welling +1
Learning the Irreducible Representations of Commutative Lie Groups
Taco Cohen, Max Welling
A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation
Max Welling, Andrew E. Gelfand, Alexander T. Ihler
Graphical Generative Adversarial Networks
Chongxuan Li, Max Welling, Jun Zhu +1