ArticleFrontiers in computational neuroscience2021
Emergence of Lie Symmetries in Functional Architectures Learned by CNNs.
Article in Frontiers in computational neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
3 citing papers in PubMed.
- ERTNet: an interpretable transformer-based framework for EEG emotion recognition.Frontiers in neuroscience · 2024Article
- Shallow Univariate ReLU Networks as Splines: Initialization, Loss Surface, Hessian, and Gradient Flow Dynamics.Frontiers in artificial intelligence · 2022Article
- ADT Network: A Novel Nonlinear Method for Decoding Speech Envelopes From EEG Signals.Trends in hearingArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper we study the spontaneous development of symmetries in the early layers of a Convolutional Neural Network (CNN) during learning on natural images. Our architecture is built in such a way to mimic some properties of the early stages of biological visual systems. In particular, it contains a pre-filtering step ℓ
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Registered trials
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.