Solving Temporal Constraints Using Neural Networks.
Bibliographic record
Abstract
There was a resurgent in research of neural nets during the late 70’s and 80’s due to advances made in learning algorithms for feed-forward and feedback networks. These advances, coupled with better computer technology, made it possible for practical applications of such networks to be made. In 1985, John Hopfield and David Tank first attempted using neural nets as an approximation method to solve optimization problems, mainly the Traveling Salesman Problem. Since then, there has been wide spread interest in applying neural nets to solve different types of optimization problems. In this paper we will mainly focus on using the Hopfield model to solve the Maximal Temporal Constraint Satisfaction Problem (MTCSP). An MTCSP is an optimization problem that consists of looking for a solution that satisfies the maximal number of temporal constraints. This can be the case of over constrained problems involving time constraints and where a complete solution does not exist, or those problems such as real time applications where a solution is needed by a given deadline. The quality of the solution (number of satisfied constraints) depends here on the time allocated for computation. Experimental comparison study of the method we propose and based on the Hopfield model with approximation methods based on local search is reported in this paper. The method based on the Hopfield model presents better results than the other methods in the case of over-constrained problems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".