Bibliographic record
Abstract
There are a variety of domains where it is desirable to learn a representation of an environment defined by a stream of sensori-motor experience. This dissertation introduces and formalizes subjective mapping, a novel approach to this problem. A learned representation is subjective if it is constructed almost entirely from the experience stream, minimizing the requirement of additional domain-specific information (which is often not readily obtainable). In many cases the observational data may be too plentiful to be feasibly stored. In these cases, a primary feature of a learned representation is that it be compact—summarizing information in a way that alleviates storage demands. Consequently, the first key insight of the subjective mapping approach is to phrase the problem as a variation of the well-studied problem of dimensionality reduction. The second insight is that knowing the effects of actions is critical to the usefulness of a representation. Therefore enforcing that actions have a consistent and succinct form in the learned representation is also a key requirement. This dissertation presents a new framework, action respecting embedding (ARE), which builds on a recent effective dimensionality reduction algorithm called maximum variance unfolding, in order to solve the newly introduced subjective mapping problem. The resulting learned representations are shown to be useful for reasoning, planning and localization tasks. At the heart of the new algorithm lies a semidefinite program leading to questions about ARE's ability to handle sufficiently large input sizes. The final contribution of this dissertation is to provide a divide-and-conquer algorithm as a first step to addressing this issue.
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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".