A Comparative Study of Restricted Hopfield Network and Dense Associative Memory
Bibliographic record
Abstract
This paper presents a comparative analysis of Restricted Hopfield Networks (RHN) and Dense Associative Memory (DAM) concerning storage capacity, time complexity, noise resilience, and the retrieval of corrupted patterns. We utilize the MNIST dataset for discrete cases and the Fashion MNIST dataset for continuous cases, selecting 10, 50, and 100 patterns from each dataset. Various configurations of RHN and DAM, with differing numbers of hidden nodes, were implemented to store these patterns. The analysis reveals that RHN and DAM employ different mechanisms for storing patterns. RHN exhibits slightly better time complexity than DAM. Our experimental results demonstrate that RHN shows a slight advantage over DAM in noise resistance and the retrieval of corrupted patterns when storing discrete patterns. However, when storing a large number of continuous patterns, DAM performs better than RHN in terms of noise resistance, while RHN outperforms DAM in retrieving corrupted patterns. Nonetheless, the performance of both models degrades as the number of hidden nodes or stored patterns increases. These findings suggest promising avenues for future research, particularly in exploring the integration of RHN and DAM into a unified model to leverage their respective strengths.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".