Max Welling

Q: What Schools Are Affiliated With Max Welling

Max Welling is affiliated with the following schools: University of Amsterdam, University of California, Berkeley, University of California, Irvine, California Institute of Technology, Utrecht University, University of Toronto

Max Welling's AcademicInfluence.com Rankings

Max Welling

Computer Science

#1042

World Rank

#1080

Historical Rank

Algorithms

#34

World Rank

#34

Historical Rank

Machine Learning

#47

World Rank

#47

Historical Rank

Artificial Intelligence

#142

World Rank

#145

Historical Rank

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Computer Science

Why Is Max Welling Influential?

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According to Wikipedia, Max Welling is a Dutch computer scientist in machine learning at the University of Amsterdam. In August 2017, the university spin-off Scyfer BV, co-founded by Welling, was acquired by Qualcomm. He has since then served as a Vice President of Technology at Qualcomm Netherlands. He is also currently the Lead Scientist of the new Microsoft Research Lab in Amsterdam.

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Max Welling's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Auto-Encoding Variational Bayes (2013) (21370)
Semi-Supervised Classification with Graph Convolutional Networks (2016) (17149)
Modeling Relational Data with Graph Convolutional Networks (2017) (2802)
Semi-supervised Learning with Deep Generative Models (2014) (2213)
Bayesian Learning via Stochastic Gradient Langevin Dynamics (2011) (2041)
Variational Graph Auto-Encoders (2016) (1942)
Improved Variational Inference with Inverse Autoregressive Flow (2016) (1456)
Group Equivariant Convolutional Networks (2016) (1280)
An Introduction to Variational Autoencoders (2019) (1065)
Variational Dropout and the Local Reparameterization Trick (2015) (1045)
Graph Convolutional Matrix Completion (2017) (891)
Attention-based Deep Multiple Instance Learning (2018) (847)
Learning Sparse Neural Networks through L0 Regularization (2017) (814)
Unsupervised Learning of Models for Recognition (2000) (768)
On Smoothing and Inference for Topic Models (2009) (605)
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis (2017) (601)
Fast collapsed gibbs sampling for latent dirichlet allocation (2008) (592)
Neural Relational Inference for Interacting Systems (2018) (587)
Attention, Learn to Solve Routing Problems! (2018) (581)
Spherical CNNs (2018) (535)
Exponential Family Harmoniums with an Application to Information Retrieval (2004) (513)
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap (2014) (492)
VAE with a VampPrior (2017) (486)
A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation (2006) (486)
Causal Effect Inference with Deep Latent-Variable Models (2017) (481)
Distributed Algorithms for Topic Models (2009) (419)
Bayesian Compression for Deep Learning (2017) (413)
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks (2017) (378)
Soft Weight-Sharing for Neural Network Compression (2017) (356)
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data (2018) (336)
Steerable CNNs (2016) (320)
Rotation Equivariant CNNs for Digital Pathology (2018) (318)
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks (2020) (310)
THE VARIATIONAL FAIR AUTOENCODER (2016) (307)
E(n) Equivariant Graph Neural Networks (2021) (302)
Gauge Equivariant Convolutional Networks and the Icosahedral CNN (2019) (295)
Towards automatic discovery of object categories (2000) (293)
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget (2013) (283)
Data-Free Quantization Through Weight Equalization and Bias Correction (2019) (275)
Stochastic Gradient VB and the Variational Auto-Encoder (2013) (260)
Distributed Inference for Latent Dirichlet Allocation (2007) (257)
Herding dynamical weights to learn (2009) (235)
Sylvester Normalizing Flows for Variational Inference (2018) (222)
Super-Samples from Kernel Herding (2010) (221)
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors (2016) (210)
Collapsed Variational Dirichlet Process Mixture Models (2007) (209)
Positive tensor factorization (2001) (195)
Contrastive Learning of Structured World Models (2019) (195)
The Variational Fair Autoencoder (2015) (195)
Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures (2010) (193)
Energy-Based Models for Sparse Overcomplete Representations (2003) (185)
Collapsed Variational Inference for HDP (2007) (181)
Asynchronous Distributed Learning of Topic Models (2008) (171)
Accelerated Variational Dirichlet Process Mixtures (2006) (163)
Learning Sparse Topographic Representations with Products of Student-t Distributions (2002) (156)
Variational Dropout and the Local Reparameterization Trick (2015) (151)
Distributed Stochastic Gradient MCMC (2014) (144)
A New Learning Algorithm for Mean Field Boltzmann Machines (2002) (143)
Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation (2013) (140)
Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation (2006) (137)
Combining generative models and Fisher kernels for object recognition (2005) (127)
Topographic Product Models Applied to Natural Scene Statistics (2006) (126)
Unsupervised learning of visual taxonomies (2008) (126)
Relaxed Quantization for Discretized Neural Networks (2018) (123)
Deep Scale-spaces: Equivariance Over Scale (2019) (120)
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation (2014) (118)
The rate adapting poisson model for information retrieval and object recognition (2006) (118)
Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation (2001) (117)
Equivariant Diffusion for Molecule Generation in 3D (2022) (117)
Integer Discrete Flows and Lossless Compression (2019) (114)
Improving Variational Auto-Encoders using Householder Flow (2016) (114)
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement (2019) (113)
DIVA: Domain Invariant Variational Autoencoders (2019) (111)
Recurrent Inference Machines for Solving Inverse Problems (2017) (105)
Learning Likelihoods with Conditional Normalizing Flows (2019) (102)
BOCK : Bayesian Optimization with Cylindrical Kernels (2018) (99)
Bayesian dark knowledge (2015) (97)
Transformation Properties of Learned Visual Representations (2014) (95)
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups (2021) (93)
Multi-HDP: A Non Parametric Bayesian Model for Tensor Factorization (2008) (91)
Learning the Irreducible Representations of Commutative Lie Groups (2014) (90)
Emerging Convolutions for Generative Normalizing Flows (2019) (89)
Bayesian k-Means as a Maximization-Expectation Algorithm (2009) (88)
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs (2020) (86)
Deep Spiking Networks (2016) (85)
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis (2016) (84)
Approximate inference in Boltzmann machines (2003) (83)
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions (2021) (79)
Computer Vision – ECCV 2016 (2016) (78)
A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence (2020) (76)
Supervised Uncertainty Quantification for Segmentation with Multiple Annotations (2019) (75)
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning (2020) (73)
On the Choice of Regions for Generalized Belief Propagation (2004) (73)
The Unified Propagation and Scaling Algorithm (2001) (71)
Hidden-Unit Conditional Random Fields (2011) (70)
Computer Vision – ECCV 2016 (2016) (70)
Combinatorial Bayesian Optimization using the Graph Cartesian Product (2019) (64)
Guided Variational Autoencoder for Disentanglement Learning (2020) (64)
Spatial Structure and Symmetry of Simple-Cell Receptive Fields in Macaque Primary Visual Cortex (2002) (63)
Viewpoint-invariant learning and detection of human heads (2000) (63)
Message Passing Neural PDE Solvers (2022) (61)
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows (2020) (60)
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets (2014) (60)
Geometric and Physical Quantities improve E(3) Equivariant Message Passing (2021) (59)
The Functional Neural Process (2019) (58)
Incremental learning of nonparametric Bayesian mixture models (2008) (58)
Attention Solves Your TSP (2018) (58)
Neural Enhanced Belief Propagation on Factor Graphs (2020) (56)
Plannable Approximations to MDP Homomorphisms: Equivariance under Actions (2020) (56)
Convolutional Networks for Spherical Signals (2017) (54)
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data (2020) (54)
Invert to Learn to Invert (2019) (54)
Sinkhorn AutoEncoders (2018) (53)
E(n) Equivariant Normalizing Flows (2021) (52)
Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models (2021) (51)
Recurrent inference machines for reconstructing heterogeneous MRI data (2019) (51)
Self Supervised Boosting (2002) (50)
A Constrained EM Algorithm for Independent Component Analysis (2001) (50)
Structured Region Graphs: Morphing EP into GBP (2005) (50)
Bayesian Bits: Unifying Quantization and Pruning (2020) (48)
Product of experts (2007) (47)
Batch-shaping for learning conditional channel gated networks (2019) (47)
Buy 4 REINFORCE Samples, Get a Baseline for Free! (2019) (47)
Generalized Darting Monte Carlo (2007) (47)
Probabilistic Binary Neural Networks (2018) (46)
A New Method to Visualize Deep Neural Networks (2016) (45)
Linear Response Algorithms for Approximate Inference in Graphical Models (2004) (43)
Estimating Gradients for Discrete Random Variables by Sampling without Replacement (2020) (43)
Deep Policy Dynamic Programming for Vehicle Routing Problems (2021) (43)
Hybrid Generative-Discriminative Visual Categorization (2008) (42)
Robust Higher Order Statistics (2005) (42)
Variational Bayes In Private Settings (VIPS) (2016) (42)
Deterministic Latent Variable Models and Their Pitfalls (2008) (42)
Data-driven Reconstruction of Gravitationally Lensed Galaxies Using Recurrent Inference Machines (2019) (41)
DP-EM: Differentially Private Expectation Maximization (2016) (41)
Combining Generative and Discriminative Models for Hybrid Inference (2019) (40)
Computer Vision – ECCV 2016 (2016) (38)
Coordinate Independent Convolutional Networks - Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds (2021) (38)
Herding Dynamic Weights for Partially Observed Random Field Models (2009) (38)
Graphical Models for Inference with Missing Data (2014) (38)
Natural Graph Networks (2020) (37)
Learning in Markov Random Fields with Contrastive Free Energies (2005) (37)
Learning in Markov Random Fields An Empirical Study (2005) (36)
On Improving the Efficiency of the Iterative Proportional Fitting Procedure (2003) (36)
Distributed and Adaptive Darting Monte Carlo through Regenerations (2013) (36)
Gradient 𝓁1 Regularization for Quantization Robustness (2020) (34)
HexaConv (2018) (34)
E(n) Equivariant Normalizing Flows for Molecule Generation in 3D (2021) (33)
The Mutual Autoencoder: Controlling Information in Latent Code Representations (2018) (33)
Graphical Generative Adversarial Networks (2018) (32)
Improving Variational Auto-Encoders using Householder Flow Improving Variational Auto-Encoders using Householder Flow (2016) (31)
Improving Variational Autoencoders with Inverse Autoregressive Flow (2016) (31)
Scalable MCMC for Mixed Membership Stochastic Blockmodels (2015) (30)
The Deep Weight Prior (2018) (30)
Hamiltonian ABC (2015) (30)
Sigma Delta Quantized Networks (2016) (29)
THE VARIATIONAL FAIR AUTO ENCODER (2015) (29)
Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation (2006) (28)
A Mode-Hopping MCMC sampler (2003) (28)
Extreme Components Analysis (2003) (27)
i-RIM applied to the fastMRI challenge (2019) (27)
Bayesian Random Fields: The Bethe-Laplace Approximation (2006) (26)
Involutive MCMC: a Unifying Framework (2020) (26)
Hybrid Variational/Gibbs Collapsed Inference in Topic Models (2008) (26)
Unsupervised Organization of Image Collections: Taxonomies and Beyond (2011) (25)
Training a Spiking Neural Network with Equilibrium Propagation (2019) (24)
A First Encounter with Machine Learning (2010) (24)
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring (2012) (24)
Attention Solves Your TSP, Approximately (2018) (24)
RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection (2020) (24)
Deep multiple instance learning for digital histopathology (2020) (23)
Robust Microbiota-Based Diagnostics for Inflammatory Bowel Disease. (2017) (22)
A New View of ICA (2001) (22)
Temporally Efficient Deep Learning with Spikes (2017) (22)
The Convolution Exponential and Generalized Sylvester Flows (2020) (22)
Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow (2017) (22)
On Herding and the Perceptron Cycling Theorem (2010) (22)
Interpretation of microbiota-based diagnostics by explaining individual classifier decisions (2017) (22)
MLitB: Machine Learning in the Browser (2014) (22)
A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration (2013) (21)
Experimental design for MRI by greedy policy search (2020) (21)
Statistical inference using weak chaos and infinite memory (2010) (20)
Localization Algorithms for Wireless Sensor Retrieval (2010) (20)
Harmonic Exponential Families on Manifolds (2015) (20)
Asynchronous distributed estimation of topic models for document analysis (2011) (20)
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference (2015) (20)
Graph Refinement based Tree Extraction using Mean-Field Networks and Graph Neural Networks (2018) (19)
Products of Edge-perts (2005) (19)
Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks (2019) (19)
Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks (2018) (19)
Extraction of Airways using Graph Neural Networks (2018) (18)
Statistical Optimization of Non-Negative Matrix Factorization (2011) (18)
Wormholes Improve Contrastive Divergence (2003) (17)
Herded Gibbs Sampling (2013) (17)
A Distributed Message Passing Algorithm for Sensor Localization (2007) (17)
Improved Bayesian Compression (2017) (17)
Lie Point Symmetry Data Augmentation for Neural PDE Solvers (2022) (16)
Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement (2020) (16)
Accelerating the BSM interpretation of LHC data with machine learning (2016) (16)
Beyond the individuality of fingerprints: a measure of simulated computer latent print source attribution accuracy (2008) (16)
Edinburgh Research Explorer Region-Based Semantic Segmentation with End-to-End Training (2016) (16)
Relational Generalized Few-Shot Learning (2019) (15)
Maximum Likelihood Estimation for the Offset-Normal Shape Distributions Using EM (2010) (15)
Topographic VAEs learn Equivariant Capsules (2021) (15)
Recurrent inference machines for accelerated MRI reconstruction. (2018) (15)
Histopathological classification of precursor lesions of esophageal adenocarcinoma: A Deep Multiple Instance Learning Approach (2018) (14)
Combinatorial Bayesian Optimization using Graph Representations (2019) (14)
Approximate Slice Sampling for Bayesian Posterior Inference (2014) (14)
Statistical Tests for Optimization Efficiency (2011) (14)
Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC (2015) (14)
MLitB: machine learning in the browser (2015) (14)
Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation (2015) (14)
Flexible Priors for Infinite Mixture Models (2006) (13)
Probabilistic sequential independent components analysis (2004) (13)
Improved Semantic Segmentation for Histopathology using Rotation Equivariant Convolutional Networks (2018) (13)
DIVA: Domain Invariant Variational Autoencoder (2019) (13)
Automatic Variational ABC (2016) (13)
Bayesian Model Scoring in Markov Random Fields (2006) (12)
Computer Vision – ECCV 2016 (2016) (12)
Memory bounded inference in topic models (2008) (12)
Mean Field Network based Graph Refinement with application to Airway Tree Extraction (2018) (12)
Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification (2017) (11)
Clifford Neural Layers for PDE Modeling (2022) (11)
Covariance in Physics and Convolutional Neural Networks (2019) (11)
The Time-Marginalized Coalescent Prior for Hierarchical Clustering (2012) (11)
Proceedings of the 26th International Conference on Neural Information Processing Systems (2013) (11)
Practical Privacy For Expectation Maximization (2016) (10)
A Data and Compute Efficient Design for Limited-Resources Deep Learning (2020) (10)
Independent Component Analysis of Incomplete Data (1999) (10)
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC (2021) (9)
Parametric Herding (2010) (9)
Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem (2019) (9)
To Relieve Your Headache of Training an MRF, Take AdVIL (2019) (9)
Multi-Agent MDP Homomorphic Networks (2021) (9)
Proceedings of the 27th International Conference on Neural Information Processing Systems (2014) (9)
Predicting simulation parameters of biological systems using a Gaussian process model (2012) (9)
Base Station Localization in Search of Empty Spectrum Spaces in Cognitive Radio Networks (2009) (9)
Scalable Overlapping Community Detection (2016) (8)
Private Topic Modeling (2016) (8)
Passing and Bouncing Messages for Generalised Inference (2001) (8)
Scalable Inference on Kingman's Coalescent using Pair Similarity (2012) (8)
Generalized Method-of-Moments for Rank Aggregation (2013) (8)
Probabilistic Numeric Convolutional Neural Networks (2020) (8)
Meta-Learning for Medical Image Classification (2018) (8)
3D scattering transforms for disease classification in neuroimaging (2017) (7)
Complex-Valued Autoencoders for Object Discovery (2022) (7)
Batch-Shaped Channel Gated Networks (2019) (7)
Gradient $\ell_1$ Regularization for Quantization Robustness (2020) (7)
Linear Response for Approximate Inference (2003) (7)
Exploiting the Statistics of Learning and Inference (2014) (7)
Efficient Parametric Projection Pursuit Density Estimation (2002) (6)
Learning Transformation Groups and their Invariants (2013) (6)
Adversarial Variational Inference and Learning in Markov Random Fields (2019) (6)
Federated Learning of User Authentication Models (2020) (6)
Federated Mixture of Experts (2021) (6)
Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks (2021) (6)
Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking (2021) (6)
Path Integral Stochastic Optimal Control for Sampling Transition Paths (2022) (6)
A deep multiple instance model to predict prostate cancer metastasis from nuclear morphology (2018) (6)
Integrating local classifiers through nonlinear dynamics on label graphs with an application to image segmentation (2011) (6)
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent (2021) (6)
Infinite State Bayes-Nets for Structured Domains (2007) (5)
UvA-DARE (Digital Academic Repository) A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration (2013) (5)
Orbital MCMC (2020) (5)
Control of C . elegans germline stem cell cycling speed meets requirements of design to minimize mutation accumulation (2015) (5)
Bayesian Inference with Big Data : A Snapshot from a Workshop (2014) (5)
Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions (2021) (5)
Simple and Accurate Uncertainty Quantification from Bias-Variance Decomposition (2020) (5)
Infinite state Bayesian networks (2007) (5)
A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation (2012) (5)
Networks of mixture blocks for non parametric bayesian models with applications (2010) (5)
Distributed Gibbs sampling for latent variable models (2012) (5)
Federated Learning of User Verification Models Without Sharing Embeddings (2021) (5)
Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders (2021) (5)
Generalized Belief Propagation on Tree Robust Structured Region Graphs (2012) (5)
Fourth International Workshop on Artificial Intelligence and Statistics (2005) (4)
Mixed Variable Bayesian Optimization with Frequency Modulated Kernels (2021) (4)
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning (2021) (4)
Quantum Deformed Neural Networks (2020) (4)
27th Annual Conference on Neural Information Processing Systems 2013: December 5-10, Lake Tahoe, Nevada, USA (2014) (4)
Modality-Agnostic Topology Aware Localization (2021) (4)
Stochastic Activation Actor Critic Methods (2019) (4)
COMBO: Combinatorial Bayesian Optimization using Graph Representations (2019) (3)
UvA-DARE ( Digital Academic Repository ) Austerity in MCMC Land : Cutting the Metropolis (2013) (3)
Herding: driving deterministic dynamics to learn and sample probabilistic models (2013) (3)
Machine Learning on Very Large Data Sets: Distributed Gibbs Sampling for Latent Variable Models (2012) (3)
Training a Network of Spiking Neurons with Equilibrium Propagation (2018) (3)
UvA-DARE (Digital Academic Repository) Neural Relational Inference for Interacting Systems Neural Relational Inference for Interacting Systems (2018) (3)
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC (2022) (3)
SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation (2022) (3)
Self Normalizing Flows (2020) (3)
Initialized Equilibrium Propagation for Backprop-Free Training (2019) (3)
An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions (2005) (3)
An Expectation-Maximization Perspective on Federated Learning (2021) (3)
A New Perspective on Uncertainty Quantification of Deep Ensembles (2020) (3)
Artificial Intelligence versus Intelligence Engineering (2019) (3)
Deterministic Gibbs Sampling via Ordinary Differential Equations (2021) (2)
Bayesian extreme components analysis (2009) (2)
Predictive Coding with Topographic Variational Autoencoders (2021) (2)
Exchangeable inconsistent priors for Bayesian posterior inference (2012) (2)
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior (2012) (2)
GCNU TR 2001 – 001 Passing And Bouncing Messages For Generalized Inference (2001) (2)
Sequential Tests for Large-Scale Learning (2016) (2)
Deep Bayesian Neural Nets as Deep Matrix Gaussian Processes (2016) (1)
Herding as a Learning System with Edge-of-Chaos Dynamics (2016) (1)
Robust Series Expansionsfor Probability Density EstimationMax WellingDept (1)
Bayesian K-Means as a "Maximization-Expectation" Algorithm (2006) (1)
A note on privacy preserving iteratively reweighted least squares (2016) (1)
2 A Bayesian Reformulation of the Platt-Burges Model (2015) (1)
UvA-DARE Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation (2015) (1)
Semisupervised Classifier Evaluation and Recalibration (2012) (1)
UvA-DARE (Digital Academic Repository) Emerging Convolutions for Generative Normalizing Flows (2019) (1)
Approximate Contrastive Free Energies for Learning in Undirected Graphical Models (2001) (1)
UvA-DARE (Digital Academic Repository) Auto-Encoding Variational Bayes Auto-Encoding Variational Bayes (2014) (1)
Addendum to “ Structured Region Graphs : Morphing EP into GBP ” (2005) (1)
The Deep Weight Prior. Modeling a prior distribution for CNNs using generative models (2018) (1)
Neural RF SLAM for unsupervised positioning and mapping with channel state information (2022) (1)
Dynamical Products of Experts for Modeling Financial Time Series (2010) (1)
Batch Bayesian Optimization on Permutations using Acquisition Weighted Kernels (2021) (1)
Draft : Manuscript is subject to change ! Structured Region Graphs : Morphing EP into GBP (2005) (1)
Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms (2014) (1)
Predictive Uncertainty through Quantization (2018) (1)
Variational Bayes in Private Settings (VIPS) (Extended Abstract) (2020) (1)
Correction for Eck et al., “Robust Microbiota-Based Diagnostics for Inflammatory Bowel Disease” (2017) (1)
CLASSEE - a Visualization Tool for Accessible Evaluation of Classification Performance (2016) (1)
Sequential Tests for Large Scale Learning (2015) (0)
UvA-DARE (Digital Academic Repository) E(n) Equivariant Graph Neural Networks (2021) (0)
UvA-DARE (Digital Academic Repository) Robust Microbiota-Based Diagnostics for Inflammatory Bowel Disease (2017) (0)
Su1781 Microbiota-Based IBD Diagnostics Proved Robust for Inter-Clinic Variation (2016) (0)
Defending Variational Autoencoders from Adversarial Attacks with MCMC (2022) (0)
UvA-DARE (Digital Academic Repository) Attention-based Deep Multiple Instance Learning (2021) (0)
UvA-DARE (Digital Academic Repository) VAE with a VampPrior VAE with a VampPrior (2018) (0)
DIVA: DOMAIN INVARIANT VARIATIONAL AUTOEN- (2019) (0)
Approximate inference by Markov chains on union spaces (2004) (0)
Sampling for ( Coupled ) Infinite Mixture Models in the Stick Breaking Representation Permalink (2006) (0)
UNIVERSITY OF CALIFORNIA IRVINE Covering Trees : New Variational Bounds for MAP Estimation in Markov Random Fields (2010) (0)
CLASSEE - Easy Evaluation of Classification Errors (2016) (0)
How Can Machine Learning Help Computer Vision in the Next Decade? (2021) (0)
Differential Equations and Continuous-Time Deep Learning (2023) (0)
Chaos for Efficient Statistical Inference and Simulation (2013) (0)
Bayesian Optimization for Macro Placement (2022) (0)
Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel (2021) (0)
UvA-DARE (Digital Academic Repository) Self Normalizing Flows (2021) (0)
UvA-DARE (Digital Academic Repository) MLitB: Machine Learning in the Browser MLitB: machine learning in the browser (2015) (0)
Explorer Modeling Relational Data with Graph Convolutional Networks (2018) (0)
Recent Advancements in Tractable Probabilistic Inference (2022) (0)
Editor's Note (2012) (0)
UvA-DARE ( Digital Academic Repository ) Graph Convolutional Matrix Completion (2017) (0)
Latent Traversals in Generative Models as Potential Flows (2023) (0)
Edinburgh Research Explorer Automatically Selecting Inference Algorithms for Discrete Energy Minimisation (2016) (0)
Help Notes for MRFLearning Code (2005) (0)
UvA-DARE (Digital Academic Repository) VAE with a VampPrior (2018) (0)
3D Equivariant Graph Neural Networks for Drug Discovery (2020) (0)
Kalman Filter (2007) (0)
Unsupervised learning of object classes from natural scenes (2010) (0)
The Deep Weight Prior - a Prior Distribution for CNNs via Generative Modeling of Parameters of the Model (2018) (0)
Spin Physics at MAMI (1991) (0)
Inference in Boltzmann Machines, Mean Field, TAP and Bethe Approximations (2007) (0)
Deep Meditations : Controlled navigation of latent space (2018) (0)
Particle Dynamics for Learning EBMs (2021) (0)
Multi-Task Bayesian Optimization (2013) (0)
UvA-DARE (Digital Academic Repository) Sequential Tests for Large Scale Learning Sequential Tests for Large Scale Learning (2015) (0)
UvA-DARE ( Digital Academic Repository ) Learning the Irreducible Representations of Commutative Lie (2014) (0)
Involutive MCMC: One Way to Derive Them All (2020) (0)
UvA-DARE (Digital Academic Multiplicative Normalizing Flows for Variational Bayesian Neural Networks (2017) (0)
DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning (2019) (0)
1 Herding for Structured Prediction (2014) (0)
The Convolution Exponential (2020) (0)
Incremental Learning of Nonparametric Bayesian Mixture Models : Extended Thesis (2011) (0)
Maximum-likelihood estimation for the offset normal shape distributions using (2006) (0)
UvA-DARE (Digital Academic Repository) The Variational Fair Autoencoder (2016) (0)
Editor's Note (2011) (0)
Responses to Reviewers on Paper Beyond Classical Diffusion : Ballistic Graph Neural Network (2019) (0)
Deep Spiking Networks (2016) (0)
Marrying Graphical Models with Deep Learning (2016) (0)
Four Lower Division Student Research Topics: Preliminary Materials from UCI's Interdisciplinary Computational Applied Mathematics Program (iCamp) (2010) (0)
Herding Dynamical Weights 2 . Maximum Entropy and Maximum Likelihood (2009) (0)
The Expectation Maximization ( EM ) algorithm (2004) (0)
Evidence Estimation for Bayesian Partially Observed MRFs (2013) (0)
Inter-Battery Topic Representation Learning (2016) (0)
Integrating Generative Modeling into Deep Learning (2020) (0)
Interpretation of microbiota-based diagnostics by explaining individual classifier decisions (2017) (0)
Geometric Clifford Algebra Networks (2023) (0)
POPE: post optimization posterior evaluation of likelihood free models (2014) (0)
UvA-DARE (Digital Academic Repository) BOCK: Bayesian Optimization with Cylindrical Kernels (2018) (0)
UvA-DARE ( Digital Academic Repository ) Bayesian posterior sampling via stochastic gradient (2012) (0)
UvA-DARE ( Digital Academic Repository ) The Variational Fair (0)
POPE: post optimization posterior evaluation of likelihood free models (2015) (0)
L G ] 6 J un 2 01 9 Covariance in Physics and Convolutional Neural Networks (2019) (0)
Hard Wall Stochastic Control based on Hallucination-EM and Power-EP (2008) (0)
Spot On: Action Localization from Pointly-Supervised Proposals (2016) (0)
UvA-DARE (Digital Academic Repository) Predictive Coding with Topographic Variational Autoencoders (2022) (0)
On the Theory and Practice of Privacy Preserving Data Analysis (2016) (0)
Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines (2023) (0)
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models (2014) (0)
A Framework for Testing Identifiability of Bayesian Models of Perception (2014) (0)

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