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Record W1985565672 · doi:10.1142/s0129183101002462

STORING PATTERNS IN HAMILTONIAN NEURAL NETWORKS

2001· article· en· W1985565672 on OpenAlexaff
A. B. Potapov, Mazhar Ali

Bibliographic record

VenueInternational Journal of Modern Physics C · 2001
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Applications
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsHamiltonian (control theory)Artificial neural networkComputer scienceCovariant Hamiltonian field theoryQuantumQuantization (signal processing)Excited stateHamiltonian systemArtificial intelligenceMathematicsPhysicsAlgorithmQuantum mechanicsClassical mechanicsMathematical optimization

Abstract

fetched live from OpenAlex

We consider a number of techniques for storing patterns in orthogonal normal modes of a Hamiltonian system. Such techniques, along with the method of selecting the most excited mode, enable us to introduce a new class of pattern recognition systems that we call Hamiltonian neural networks. In contrast to all traditional neural networks, our Hamiltonian neural networks are nondissipative. Quantization of the Hamiltonian of our network is expected to serve as a means for future work on quantum analogs of classical information processing.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.763
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.272
Teacher spread0.250 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2001
Admission routes1
Has abstractyes

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