ArticleProceedings of the National Academy of Sciences of the United States of America2021
Neural network interpretation using descrambler groups.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- High-confidence reconstruction for Laplace inversion in NMR based on uncertainty-informed deep learning.Science advances · 2025Article
- An enhanced transcription factor repressilator that buffers stochasticity and entrains to an erratic external circadian signal.Frontiers in systems biology · 2023Article
- The ABC transporter MsbA adopts the wide inward-open conformation inScience advances · 2022Article
- Cross-validation of distance measurements in proteins by PELDOR/DEER and single-molecule FRET.Nature communications · 2022Article
- Towards autonomous analysis of chemical exchange saturation transfer experiments using deep neural networks.Journal of biomolecular NMR · 2022Article
- Deep-Learning-Assisted Focused Ion Beam Nanofabrication.Nano letters · 2022Article
- Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function?Current issues in molecular biology · 2022Review
- Protein functional dynamics from the rigorous global analysis of DEER data: Conditions, components, and conformations.The Journal of general physiology · 2021Article
- FID-Net: A versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling.Journal of biomolecular NMR · 2021Article
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Authors and funding
4 authors.
Funding
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Abstract
The lack of interpretability and trust is a much-criticized feature of deep neural networks. In fully connected nets, the signaling between inner layers is scrambled because backpropagation training does not require perceptrons to be arranged in any particular order. The result is a black box; this problem is particularly severe in scientific computing and digital signal processing (DSP), where neural nets perform abstract mathematical transformations that do not reduce to features or concepts. We present here a group-theoretical procedure that attempts to bring inner-layer signaling into a human-readable form, the assumption being that this form exists and has identifiable and quantifiable features-for example, smoothness or locality. We applied the proposed method to DEERNet (a DSP network used in electron spin resonance) and managed to descramble it. We found considerable internal sophistication: the network spontaneously invents a bandpass filter, a notch filter, a frequency axis rescaling transformation, frequency-division multiplexing, group embedding, spectral filtering regularization, and a map from harmonic functions into Chebyshev polynomials-in 10 min of unattended training from a random initial guess.
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