An Analysis of Internal Representations for Two Artificial Neural Networks that Classify Musical Chords
Notice bibliographique
Résumé
Cognitive informatics is a field of research that is primarily concerned with the information processing of intelligent agents; it can be characterised in terms of an evolving notion of information (Wang, 2007). When it originated six decades ago, conventional accounts of information were concerned about using probability theory and statistics to measure the amount of information carried by an external signal. This, in turn, developed into the notion of modern informatics which studied information as “properties or attributes of the natural world that can be generally abstracted, quantitatively represented, and mentally processed” (Wang, 2007, p. iii). The current incarnation of cognitive informatics recognised that both information theory and modern informatics defined information in terms of factors that were external to brains, and has replaced this with an emphasis on exploring information as an internal property. This emphasis on the internal processing of information raises fundamental questions about how such information can be represented. One approach to answering such questions — and for proposing new representational accounts — would be to train a brain-like system to perform an intelligent task, and then to analyse its internal structure to determine the types of representations that the system had developed to perform this intelligent behaviour. The logic behind this approach is that when artificial neural networks Cognitive informatics is a field of research that is primarily concerned with the information processing of intelligent agents; it can be characterised in terms of an evolving notion of information (Wang, 2007). When it originated six decades ago, conventional accounts of information were concerned about using probability theory and statistics to measure the amount of information carried by an external signal. This, in turn, developed into the notion of modern informatics which studied information as “properties or attributes of the natural world that can be generally abstracted, quantitatively represented, and mentally processed” (Wang, 2007, p. iii). The current incarnation of cognitive informatics recognised that both information theory and modern informatics defined information in terms of factors that were external to brains, and has replaced this with an emphasis on exploring information as an internal property. This emphasis on the internal processing of information raises fundamental questions about how such information can be represented. One approach to answering such questions — and for proposing new representational accounts — would be to train a brain-like system to perform an intelligent task, and then to analyse its internal structure to determine the types of representations that the system had developed to perform this intelligent behaviour. The logic behind this approach is that when artificial neural networks
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 source (Gemma direct ou Codex distillé), 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 ».