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Life sciences · Preprint
arXiv · September 4, 2026
Raises a question worth testing. It does not answer one.
This is an unpeer-reviewed preprint describing a novel unsupervised method for selecting neurons in overparameterized networks by minimizing mapping entropy. The approach is demonstrated on synthetic teacher-student tasks and translation-augmented MNIST, showing that mapping-entropy-selected subnetworks outperform random subsets under strong compression, but the work remains early-stage theory without validation on real networks or established benchmarks.
Computational theory and simulation study. Intervention: Mapping entropy (ME) minimisation applied to select neuron subsets from hidden layers in teacher-student networks and non-linear Gaussian process tasks.. Compared with: Random neuron subsets of equal size..
Mapping entropy (ME) optimisation recovers minimal teacher-consistent representation in teacher-student networks. ME-selected subnetworks outperform random subsets of equal size, most clearly under strong compression. ME criterion is fully unsupervised, depending only on hidden-activation statistics without labels or gradients.
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The source did not state who this applies to in practice.
A theoretical and computational study proposing an unsupervised neuron selection method based on mapping entropy, with proof-of-concept results on synthetic and toy datasets, but no clinical or established-domain validation.
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Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Overparameterized neural networks carry far more hidden units than a task nominally requires, raising the question of which neurons are essential and whether that distinction is legible in the representation itself, without labels or gradients. We cast neuron selection as the problem of coarse-graining the hidden layer by retaining a subset of its neurons, and score each putative selection by the mapping entropy (ME). This quantity measures the loss of discriminatory power inherent in discarding part of the network neurons, and the selection that minimises the ME is taken as particularly informative. This criterion is fully unsupervised, in that it depends only on hidden-activation statistics. In teacher-student networks, ME optimisation recovers the minimal teacher-consistent representation and retains extra units in proportion to the hidden layer's residual variability; in a non-linear Gaussian process task, it selects coherent functional-class mappings whose preferred class shifts across training. On this task and on translation-augmented MNIST, ME-selected subnetworks outperform random subsets of equal size, most clearly under strong compression - linking configurational distinguishability to predictive performance.
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