ArticleEntropy (Basel, Switzerland)2020
Examining the Causal Structures of Deep Neural Networks Using Information Theory.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Quantifying emergent complexity.Patterns (New York, N.Y.) · 2026Article
- Intrinsic units: identifying a system's causal grain.Neuroscience of consciousness · 2026Article
- Finding emergence in data by maximizing effective information.National science review · 2025Article
- Emergence and Causality in Complex Systems: A Survey of Causal Emergence and Related Quantitative Studies.Entropy (Basel, Switzerland) · 2024Review
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3 authors.
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Abstract
Deep Neural Networks (DNNs) are often examined at the level of their response to input, such as analyzing the mutual information between nodes and data sets. Yet DNNs can also be examined at the level of causation, exploring "what does what" within the layers of the network itself. Historically, analyzing the causal structure of DNNs has received less attention than understanding their responses to input. Yet definitionally, generalizability must be a function of a DNN's causal structure as it reflects how the DNN responds to unseen or even not-yet-defined future inputs. Here, we introduce a suite of metrics based on information theory to quantify and track changes in the causal structure of DNNs during training. Specifically, we introduce the
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.