Réduction de la complexité spatiale et temporelle du Compact Prediction Tree pour la prédiction de séquences.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Resume. La prediction de sequences de symboles est une tâche ayant de multiples applications. Plusieurs modeles de prediction ont ete proposes tels que DG, All-k-order markov et PPM. Recemment, il a ete montre qu’un nouveau modele nomme Compact Prediction Tree (CPT) utilisant une structure en arbre et un algorithme de prediction plus complexe, offre des predictions plus exactes que plusieurs approches de la litterature. Neanmoins, une limite importante de CPT est sa complexite temporelle et spatiale elevee. Dans cet article, nous pallions ce probleme en proposant trois strategies pour reduire la taille et le temps de prediction de CPT. Les resultats experimentaux sur 7 jeux de donnees reels montrent que le modele resultant nomme CPT+ est jusqu’a 98 fois plus compact et est 4.5 fois plus rapide que CPT, tout en conservant une exactitude tres elevee par rapport a All-K-order Markov, DG, Lz78, PPM et TDAG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it