{"slug": "neural-feature-governance-extending-atom-prevalence", "title": "Neural Feature Governance: Extending Atom Prevalence", "summary": "A new Bayesian framework called Neural Atom Prevalence (NAP) achieves state-of-the-art structural sparsity in feedforward neural networks, reducing active nodes to as few as 8% of the original dense architecture on MNIST while maintaining near-nominal predictive interval coverage (93.4% observed against a 95% target). The method, introduced in arXiv:2607.21671v1, also provides well-calibrated uncertainty quantification with model ignorance accounting for only 3-4% of total predictive variance.", "body_md": "arXiv:2607.21671v1 Announce Type: new\nAbstract: Neural network compression and interpretability remain open challenges in modern deep learn- ing, where billion-parameter architectures deliver impressive accuracy at the cost of trans- parency, computational efficiency, and reliable uncertainty quantification. This paper introduces Neural Atom Prevalence (NAP), a principled Bayesian framework for structured node-level model selection in feedforward neural networks. NAP introduces the neural atom (activation unit) and functions as a hybrid method operating through a four-phase pipeline: Bayesian Lottery Ticket (BLT) identification via Iterative Magnitude Pruning (IMP), soft variational training of the Spike and Slab Independent Gaussian (SS-IG) model, Poisson-Binomial (PB) optimal layer-size selection, and Bayesian fine-tuning to produce a sparse, stable, interpretable, and accurate model. Extensive empirical validation across simulated nonlinear regression, two UCI benchmark datasets (Concrete, YearPredictionMSD), and the MNIST image classification task demonstrates that NAP achieves state-of-the-art structural sparsity, reducing active nodes to as few as 8% of the original dense architecture on MNIST, while well-calibrated probabilisti- cally: the aleatoric-epistemic uncertainty decomposition reveals that model ignorance accounts for only 3 to 4% of total predictive variance across all experiments, and regression reliability diagrams confirm a near-nominal predictive interval coverage (93.4% observed against a 95% target). These results establish NAP as a reliable, theoretically grounded, and computation- ally tractable solution to the simultaneous pursuit of sparsity, accuracy, interpretability, and uncertainty quantification in Bayesian neural networks.", "url": "https://wpnews.pro/news/neural-feature-governance-extending-atom-prevalence", "canonical_source": "https://arxiv.org/abs/2607.21671", "published_at": "2026-07-27 04:00:00+00:00", "updated_at": "2026-07-27 04:08:55.019329+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "ai-research"], "entities": ["Neural Atom Prevalence", "arXiv", "UCI", "MNIST"], "alternates": {"html": "https://wpnews.pro/news/neural-feature-governance-extending-atom-prevalence", "markdown": "https://wpnews.pro/news/neural-feature-governance-extending-atom-prevalence.md", "text": "https://wpnews.pro/news/neural-feature-governance-extending-atom-prevalence.txt", "jsonld": "https://wpnews.pro/news/neural-feature-governance-extending-atom-prevalence.jsonld"}}