Learning Graphical Models
On Learning Markov Chains
Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati
Neural Information Processing Systems http://nips.cc/
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann
Neural Information Processing Systems http://nips.cc/
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.