Learning Graphical Models
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen
Graph structured data are abundant in the real world.
Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
While humans accomplish 72.4% of the tasks, the best
A neurally plausible model learns successor representations in partially observable environments
Neural Information Processing Systems http://nips.cc/
Amortized Bethe Free Energy Minimization for Learning MRFs
Neural Information Processing Systems http://nips.cc/
Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
Notably, most real-world homophilic and heterophilic graphs are comprised of a mixture of nodes in both homophilic and heterophilic structural patterns, exhibiting a structural disparity.
The Functional Neural Process
Christos Louizos, Xiahan Shi, Klamer Schutte, Max Welling
Neural Information Processing Systems http://nips.cc/
Deep Homogeneous Mixture Models: Representation, Separation, and Approximation
Priyank Jaini, Pascal Poupart, Yaoliang Yu
Neural Information Processing Systems http://nips.cc/
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Dominik Linzner, Michael Schmidt, Heinz Koeppl
Instead ofsampling andscoring allpossible structures individually,we assume the generator of the CTBN to be composed as a mixture of generators stemming from different structures.
