A neural network shortest path algorithm for optimum routing in packet-switched communications networks
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Bibliographic record
Abstract
The authors consider the application of neural networks to the optimum routing problem in packet-switched communications networks, where the goal is to minimize the network-wide average time delay. Under appropriate assumptions it is shown that the optimum routing algorithm relies heavily on shortest path computations, which have to be carried out in real time. For this purpose an efficient neural network shortest path algorithm based on the Hopfield model is proposed, which is an improved version of previously suggested neural algorithms. The general principles involved in the design of the proposed neural network are discussed. The computational power of the proposed neural model is demonstrated through computer simulations. It is noted that the neural network approach will enable the communications engineer to benefit from the inherent features of neural networks, namely a potential for high computation power and speed, a high degree of robustness and fault tolerance, low power consumption, and real-time operation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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