2013 · Generative models
Auto-Encoding Variational Bayes
Variational autoencoders provide a tractable approach to learning probabilistic latent representations and generating data.
Paper & contextMax Welling
Using probabilistic latent spaces to model observations.
Welling studies probabilistic learning, structured representations, and generative models. His coauthored work includes variational autoencoders and graph convolutional networks. The selected papers explore how mathematical structure can guide what a model learns and how efficiently it learns it.
Selected work
10 papers
2016 · Generative models
Semi-Supervised Classification with Graph Convolutional Networks
Selected research in generative models. Read the full paper, including the methods, experiments, and reported results.
Paper & context2017 · Generative models
Modeling Relational Data with Graph Convolutional Networks
Relational graph convolutional networks learn from graphs whose edges represent different kinds of relationships.
Paper & context2026 · Generative models
Stochastic Counterdiabatic Driving via Biorthogonal Liouvillian Eigenmodes
Selected research in generative models. Read the full paper, including the methods, experiments, and reported results.
Paper & context2026 · Generative models
AVQ-Attention: Adaptive Vector-Quantized Attention
Selected research in generative models. Read the full paper, including the methods, experiments, and reported results.
Paper & context2026 · Generative models
QMaxCal: Path-Space Regularization for Open Quantum Control via Girsanov's Theorem
Selected research in generative models. Read the full paper, including the methods, experiments, and reported results.
Paper & context2026 · Generative models
Discovering and decoding latent mean-field structure with variational autoencoders
Selected research in generative models. Read the full paper, including the methods, experiments, and reported results.
Paper & context2026 · Generative models
Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Models
Selected research in generative models. Read the full paper, including the methods, experiments, and reported results.
Paper & context2026 · Generative models
Flowing with Confidence
Selected research in generative models. Read the full paper, including the methods, experiments, and reported results.
Paper & context2026 · Generative models
Spontaneous symmetry breaking and Goldstone modes for deep information propagation
Selected research in generative models. Read the full paper, including the methods, experiments, and reported results.
Paper & contextAn independent editorial profile. Inclusion does not imply Council membership or endorsement. Research is collaborative; coauthorship does not imply sole credit.
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