Learning Graphical Models
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. (Wikipedia)
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Spectral Filtering for General Linear Dynamical Systems
Elad Hazan, Holden Lee, Karan Singh, Cyril Zhang, Yi Zhang
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On Learning Markov Chains
Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati
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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
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D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen
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Graph structured data are abundant in the real world.
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Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
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While humans accomplish 72.4% of the tasks, the best
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A neurally plausible model learns successor representations in partially observable environments
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