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.Thank you first and foremost to my thesis supervisor Dr. Robert L. West.Your continued encouragement to follow my curiosity and to ask strange questions about strange topics helped me find the deeper connections I sought, not only concerning the topics discussed here, but also those related to cognition, artificial intelligence, psychology, narrative, spirituality, and about life in general.All the time spent with you talking in coffee shops, on walks in the park and by the canal, and even during aimless strolls through neighborhoods was immensely rewarding, and I am grateful to you for all the experiences we shared.Thank you to my wife Katie and to my mom Ayten for your endless love, patience, and continued support in many ways over the last few years of my academic journey.My family is truly my foundation-I cannot fully express here the level of appreciation I have for what you have done for me to help me accomplish my goals.I love you both so much.I must also thank my thesis committee members Dr. Mary A. Kelly and Dr. Angelo B. Mingarelli.Your feedback of my work was essential in reaching the finish line with a more polished thesis document, and our discussions helped me better understand the larger context of my work, especially regarding how it could be communicated to a general academic audience.I am also grateful to Dr. Kelly for exposing me to Vector Symbolic Architectures in the first place, and for giving me the opportunity to collaborate with you and to discuss exciting research.Last but not least, I want to
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".