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Found 3 papers, 2 papers with code
Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies
We develop a nonparametric identifiability theory that formalizes this principle and shows that the latent factors can be recovered by regularizing the learned causal graph to be sparse.
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA
This work introduces a novel principle we call disentanglement via mechanism sparsity regularization, which can be applied when the latent factors of interest depend sparsely on past latent factors and/or observed auxiliary variables.
Pay attention to the activations: a modular attention mechanism for fine-grained image recognition
Attention has been typically implemented in neural networks by selecting the most informative regions of the image that improve classification.
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