ZIZO: A Novel Zoom-In–Zoom-Out Search Algorithm for the Global Parameters of Echo-State Networks
Bibliographic record
Abstract
Echo-state networks (ESNs) are a distinct architecture for recurrent neural networks (RNNs). The great advantage of ESN is that they offer an easy way to train the RNN. To make full use of ESN, one needs to first identify their global (hyper) parameters. These are input scaling, leaking rate (for leaky ESN), spectral radius, and the size of the ESN. The most recommended way to get their optimal (or suboptimal) values is by trial-and-error. However, in practice, this method has a very low efficiency. In order to tackle this problem, we propose a novel “zoom-in-zoom-out” (ZIZO) algorithm for generating the global parameters automatically. The proposed technique consists of two major parts. First, we generate random ranges for the parameters of ESNs. Then, based on bootstrap-sampling, we search the optimal solution within the fixed specific ranges. To evaluate the proposed method, we use two different data sets, which are collected from the literature. The obtained results demonstrate the efficiency and accuracy of ZIZO.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".