A Recurrent Dynamic Model for Efficient Bayesian Optimization<sup>*</sup>
Why this work is in the frame
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Bibliographic record
Abstract
Bayesian optimization is an important black-box optimization method used in active learning. An implementation of the algorithm using vector embeddings from Vector Symbolic Architectures was proposed as an efficient, neuromorphic approach to solving these implementation problems. However, a clear path to neural implementation has not been explicated. In this paper, we explore an implementation of this algorithm expressed as recurrent dynamics that can be easily translated to neural populations, and present an implementation within the Lava programming framework for Intel’s neuromorphic computers. We compare the performance of the algorithm using different resolution representations of real-valued data, and demonstrate that the ability to find optima is preserved. This work provides a path forward to the implementation of Bayesian optimization on low-power neuromorphic computers, permitting the deployment of active learning techniques in low-power, edge computing applications.
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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 | 0.000 | 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 it