Bibliographic record
Abstract
In this thesis, I explore and extend the methods of Vector Symbolic Architectures (VSAs), with a particular focus on Holographic Reduced Representations (HRRs) and group VSAs.Through a consideration of abstract groups and linear representation theory, I provide detailed analyses of these systems to clarify their underlying algebraic structures, and suggest a unifying framework that enables the construction of new HRR-like VSA systems.In addition I present novel VSA models, including a memory retrieval model based on Hopfield networks for use with unitary HRRs and other group VSA generalizations, and a resonator network model that solves for rotations in 3D space.I also introduce unitary group VSAs, and show that such VSAs satisfy the notion of a maximally expressive VSA system under certain formal assumptions, enabling the representation of a diverse set of structured data relationships.i Although I am the sole author of this work, the process that culminated in this thesis involved many people who in their own ways contributed to the words on these pages.Without their support, the completion of this thesis would not have been possible.Below, I acknowledge some of these influential people, although there are many others to whom I owe my gratitude.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".