System-level design of low complexity CVNS feed forward neural network
Bibliographic record
Abstract
In order to optimally set up and configure an analog neural network in system-level, fundamental issues such as accuracy, robustness, function smoothness and minimality has to be considered. This paper focuses on choosing optimal Continuous Valued Number System (CVNS) neural networks, and shows using system-level analysis that how CVNS networks can be used to implement large size networks. The network is implemented using analog non-linear activation function with more precision, and provides more accuracy in comparison to analog networks. The CVNS computation system which is used as an alternative method of implementation, is analog in nature and employs digit-level analog modular arithmetic. The information redundancy among the digits can be used to increase the accuracy of the precision using analog circuitry with arbitrary accuracy. Moreover, the system configuration take advantage of distributed neuron properties. This type of neurons reduce overall network sensitivity to mismatches that are inherent in any neural networks implemented by analog circuitries. Moreover, to reduce the network complexity in terms of number of interconnections, a series configuration of multiplexer and demultiplexer is used. Weights are refreshed and refined as an overall approach to maintain the weights stored on chip, and are not used to compute network response. To study overall accuracy of the system, stochastic modeling of the network is carried out. The proposed network has comparable sensitivity to other CVNS Madaline, while reduces the network complexity in terms of reducing computing units and interconnections proportional by a factor proportional to the network nodes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".