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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Reward is Enough for Convex MDPs
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Maximising a cumulative reward function that is Markov and stationary, i.e., defined over state-action pairs and independent of time, is sufficient to capture many
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Reliable training and estimation of variance networks
Nicki Skafte, Martin Jørgensen, Søren Hauberg
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Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections
Raanan Yehezkel Rohekar, Yaniv Gurwicz, Shami Nisimov, Gal Novik
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NodeFormer: AScalable Graph Structure Learning Transformerfor Node Classification
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Appendix C 4. Ifyouareusingexistingassets (e.g., code, data, models) orcurating/releasingnewassets... (a) Ifyourworkusesexistingassets, didyoucitethecreators?[Yes]See
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