Max Welling

Artificial Kuramoto Oscillatory Neurons ICLR 2025 Controlled Generation with Equivariant Variational Flow Matching ICML 2025 Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems ICML 2025 BARNN: A Bayesian Autoregressive and Recurrent Neural Network ICML 2025 Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI ICML 2024 Variational Flow Matching for Graph Generation NIPS 2024 Traveling Waves Encode The Recent Past and Enhance Sequence Learning ICLR 2024 Protect Your Score: Contact-Tracing with Differential Privacy Guarantees AAAI 2024 GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers ICLR 2024 Flow Factorized Representation Learning NIPS 2023 Rotating Features for Object Discovery NIPS 2023 Lie Point Symmetry and Physics-Informed Networks NIPS 2023 Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths NIPS 2023 No time to waste: practical statistical contact tracing with few low-bit messages AISTATS 2023 Latent Traversals in Generative Models as Potential Flows ICML 2023 Geometric Clifford Algebra Networks ICML 2023 Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks ICML 2023 Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation NIPS 2023 Clifford Neural Layers for PDE Modeling ICLR 2023 Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel NIPS 2022 Orbital MCMC AISTATS 2022 Equivariant Diffusion for Molecule Generation in 3D ICML 2022 Lie Point Symmetry Data Augmentation for Neural PDE Solvers ICML 2022 Message Passing Neural PDE Solvers ICLR 2022 Geometric and Physical Quantities improve E(3) Equivariant Message Passing ICLR 2022 Multi-Agent MDP Homomorphic Networks ICLR 2022 Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data CLEAR 2022 On the symmetries of the synchronization problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane NIPS 2022 Alleviating Adversarial Attacks on Variational Autoencoders with MCMC NIPS 2022 Mixed variable Bayesian optimization with frequency modulated kernels UAI 2021 Neural Enhanced Belief Propagation on Factor Graphs AISTATS 2021 Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC AISTATS 2021 E(n) Equivariant Normalizing Flows NIPS 2021 Modality-Agnostic Topology Aware Localization NIPS 2021 Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions NIPS 2021 Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent NIPS 2021 Topographic VAEs learn Equivariant Capsules NIPS 2021 Self Normalizing Flows ICML 2021 The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning ICML 2021 E(n) Equivariant Graph Neural Networks ICML 2021 A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups ICML 2021 Federated Learning of User Verification Models Without Sharing Embeddings ICML 2021 Probabilistic Numeric Convolutional Neural Networks ICLR 2021 Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs ICLR 2021 Contrastive Learning of Structured World Models ICLR 2020 DIVA: Domain Invariant Variational Autoencoders MIDL 2020 Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement JMLR 2020 Variational Bayes in Private Settings (VIPS) (Extended Abstract) IJCAI 2020 SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks NIPS 2020 Natural Graph Networks NIPS 2020 MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning NIPS 2020 Bayesian Bits: Unifying Quantization and Pruning NIPS 2020 SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows NIPS 2020 The Convolution Exponential and Generalized Sylvester Flows NIPS 2020 Experimental design for MRI by greedy policy search NIPS 2020 Involutive MCMC: a Unifying Framework ICML 2020 Guided Variational Autoencoder for Disentanglement Learning CVPR 2020 Gradient $\ell_1$ Regularization for Quantization Robustness ICLR 2020 Batch-shaping for learning conditional channel gated networks ICLR 2020 Estimating Gradients for Discrete Random Variables by Sampling without Replacement ICLR 2020 To Relieve Your Headache of Training an MRF, Take AdVIL ICLR 2020 Deep Scale-spaces: Equivariance Over Scale NIPS 2019 The Functional Neural Process NIPS 2019 Gauge Equivariant Convolutional Networks and the Icosahedral CNN ICML 2019 Initialized Equilibrium Propagation for Backprop-Free Training ICLR 2019 Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem UAI 2019 Invert to Learn to Invert NIPS 2019 Combining Generative and Discriminative Models for Hybrid Inference NIPS 2019 Integer Discrete Flows and Lossless Compression NIPS 2019 Combinatorial Bayesian Optimization using the Graph Cartesian Product NIPS 2019 Training a Spiking Neural Network with Equilibrium Propagation AISTATS 2019 Sinkhorn AutoEncoders UAI 2019 Emerging Convolutions for Generative Normalizing Flows ICML 2019 Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement ICML 2019 Data-Free Quantization Through Weight Equalization and Bias Correction ICCV 2019 The Deep Weight Prior ICLR 2019 Relaxed Quantization for Discretized Neural Networks ICLR 2019 Attention, Learn to Solve Routing Problems! ICLR 2019 Graphical Generative Adversarial Networks NIPS 2018 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data NIPS 2018 Spherical CNNs ICLR 2018 HexaConv ICLR 2018 Temporally Efficient Deep Learning with Spikes ICLR 2018 Learning Sparse Neural Networks through L_0 Regularization ICLR 2018 VAE with a VampPrior AISTATS 2018 BOCK : Bayesian Optimization with Cylindrical Kernels ICML 2018 Attention-based Deep Multiple Instance Learning ICML 2018 Neural Relational Inference for Interacting Systems ICML 2018 Bayesian Compression for Deep Learning NIPS 2017 Causal Effect Inference with Deep Latent-Variable Models NIPS 2017 DP-EM: Differentially Private Expectation Maximization AISTATS 2017 Multiplicative Normalizing Flows for Variational Bayesian Neural Networks ICML 2017 Improved Variational Inference with Inverse Autoregressive Flow NIPS 2016 Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors ICML 2016 Group Equivariant Convolutional Networks ICML 2016 Herded Gibbs Sampling JMLR 2016 Scalable MCMC for Mixed Membership Stochastic Blockmodels AISTATS 2016 Harmonic Exponential Families on Manifolds ICML 2015 Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference NIPS 2015 Bayesian dark knowledge NIPS 2015 Variational Dropout and the Local Reparameterization Trick NIPS 2015 Markov Chain Monte Carlo and Variational Inference: Bridging the Gap ICML 2015 Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets ICML 2014 Learning the Irreducible Representations of Commutative Lie Groups ICML 2014 Distributed Stochastic Gradient MCMC ICML 2014 Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget ICML 2014 Approximate Slice Sampling for Bayesian Posterior Inference AISTATS 2014 Semi-supervised Learning with Deep Generative Models NIPS 2014 A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration CVPR 2013 Distributed and Adaptive Darting Monte Carlo through Regenerations AISTATS 2013 Evidence Estimation for Bayesian Partially Observed MRFs AISTATS 2013 The Time-Marginalized Coalescent Prior for Hierarchical Clustering NIPS 2012 Scalable Inference on Kingman’s Coalescent using Pair Similarity AISTATS 2012 Hidden-Unit Conditional Random Fields AISTATS 2011 Statistical Optimization of Non-Negative Matrix Factorization AISTATS 2011 Statistical Tests for Optimization Efficiency NIPS 2011 On Herding and the Perceptron Cycling Theorem NIPS 2010 Parametric Herding AISTATS 2010 Distributed Algorithms for Topic Models JMLR 2009 Asynchronous Distributed Learning of Topic Models NIPS 2008 Infinite State Bayes-Nets for Structured Domains NIPS 2007 Collapsed Variational Inference for HDP NIPS 2007 Distributed Inference for Latent Dirichlet Allocation NIPS 2007 A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation NIPS 2006 Accelerated Variational Dirichlet Process Mixtures NIPS 2006 Bayesian Model Scoring in Markov Random Fields NIPS 2006 Energy-Based Models for Sparse Overcomplete Representations JMLR 2003