Statistical signatures of abstraction in deep neural networks

• URL: http://arxiv.org/abs/2407.01656v2
• Date: Tue, 01 Oct 2024 12:39:15 GMT
• Title: Statistical signatures of abstraction in deep neural networks
• Authors: Carlo Orientale Caputo, Matteo Marsili,
• Abstract summary: We study how abstract representations emerge in a Deep Belief Network (DBN) trained on benchmark datasets. We show that the representation approaches an universal model determined by the principle of maximal relevance. We also show that plasticity increases with depth, in a similar way as it does in the brain.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study how abstract representations emerge in a Deep Belief Network (DBN) trained on benchmark datasets. Our analysis targets the principles of learning in the early stages of information processing, starting from the "primordial soup" of the under-sampling regime. As the data is processed by deeper and deeper layers, features are detected and removed, transferring more and more "context-invariant" information to deeper layers. We show that the representation approaches an universal model -- the Hierarchical Feature Model (HFM) -- determined by the principle of maximal relevance. Relevance quantifies the uncertainty on the model of the data, thus suggesting that "meaning" -- i.e. syntactic information -- is that part of the data which is not yet captured by a model. Our analysis shows that shallow layers are well described by pairwise Ising models, which provide a representation of the data in terms of generic, low order features. We also show that plasticity increases with depth, in a similar way as it does in the brain. These findings suggest that DBNs are capable of extracting a hierarchy of features from the data which is consistent with the principle of maximal relevance.

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