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Record W2154766171 · doi:10.1109/tcsii.2010.2067775

Resistive-Type CVNS Distributed Neural Networks With Improved Noise-to-Signal Ratio

2010· article· en· W2154766171 on OpenAlexaff
Golnar Khodabandehloo, Mitra Mirhassani, Majid Ahmadi

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2010
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Applications
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsArtificial neural networkResistive touchscreenComputer scienceNoise (video)Sensitivity (control systems)SIGNAL (programming language)ScalingType (biology)Artificial intelligenceElectronic engineeringMathematicsEngineering

Abstract

fetched live from OpenAlex

Resistive-type distributed neural networks (DNNs) provide a self-scaling structure for the neuron, which can spontaneously adapt itself to different numbers of inputs. In lumped neural networks, the neuron should be changed whenever the number of inputs changes due to the applications; redesigning the neuron is not practical, particularly for hardware implementations. In this brief, a group of feedforward DNNs based on a continuous valued number system is proposed, which outperforms not only the lumped neural networks but also the conventional DNNs because of the reduced sensitivity to noise.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.222
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2010
Admission routes1
Has abstractyes

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