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Found 4 papers, 1 papers with code
CLEAR: Causal Explanations from Attention in Neural Recommenders
We present CLEAR, a method for learning session-specific causal graphs, in the possible presence of latent confounders, from attention in pre-trained attention-based recommenders.
Bayesian Structure Learning by Recursive Bootstrap
The proposed method deals with the main weakness of constraint-based learning---sensitivity to errors in the independence tests---by a novel way of combining bootstrap with constraint-based learning.
Constructing Deep Neural Networks by Bayesian Network Structure Learning
We prove that conditional-dependency relations among the latent variables in the generative graph are preserved in the class-conditional discriminative graph.
Unsupervised Deep Structure Learning by Recursive Dependency Analysis
Finally, a deep neural network structure is constructed based on the discriminative graph.
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