Structural network measures reveal the emergence of heavy-tailed degree distributions in lottery ticket multilayer perceptrons
Notice bibliographique
Résumé
Artificial neural networks (ANNs) were originally modeled after their biological counterparts, but have since conceptually diverged in many ways. The resulting network architectures are not well understood, and furthermore, we lack the quantitative tools to characterize their structures. Network science provides an ideal mathematical framework with which to characterize systems of interacting components, and has transformed our understanding across many domains, including the mammalian brain. Yet, little has been done to bring network science to ANNs. In this work, we propose tools that leverage and adapt network science methods to measure both global- and local-level characteristics of ANNs. Specifically, we focus on the structures of efficient multilayer perceptrons as a case study, which are sparse and systematically pruned such that they share many characteristics with real-world networks. We use adapted network science metrics to show that the pruning process leads to the emergence of a spanning subnetwork (lottery ticket multilayer perceptrons) with complex architecture. This complex network exhibits global and local characteristics, including heavy-tailed nodal degree distributions and dominant weighted pathways, that mirror patterns observed in human neuronal connectivity. Furthermore, alterations in network metrics precede catastrophic decay in performance as the network is heavily pruned. This network science-driven approach to the analysis of artificial neural networks serves as a valuable tool to establish and improve biological fidelity, increase the interpretability, and assess the performance of artificial neural networks. Significance Statement Artificial neural network architectures have become increasingly complex, often diverging from their biological counterparts in many ways. To design plausible "brain-like" architectures, whether to advance neuroscience research or to improve explainability, it is essential that these networks optimally resemble their biological counterparts. Network science tools offer valuable information about interconnected systems, including the brain, but have not attracted much attention for analyzing artificial neural networks. Here, we present the significance of our work: •We adapt network science tools to analyze the structural characteristics of artificial neural networks. •We demonstrate that organizational patterns similar to those observed in the mammalian brain emerge through the pruning process alone. The convergence on these complex network features in both artificial neural networks and biological brain networks is compelling evidence for their optimality in information processing capabilities. •Our approach is a significant first step towards a network science-based understanding of artificial neural networks, and has the potential to shed light on the biological fidelity of artificial neural networks.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».