Parallel Algorithm for a Hidden Markov Model with an Indefinite Number of States and Heterogeneous Observation Data
Why this work is in the frame
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Bibliographic record
Abstract
In addition to being a modern technique used in speech recognition applications, Hidden Markov Models (HMMs) are widely used in other areas to predict equipment life cycles and optimize maintenance, for example. Problems of this type have a very limited and fragmented set of observable data, as well as limited information on the possible states of the system. This article proposes a strategy for organizing HMM parallel learning, which is effectively implemented using OpenCL on GPU devices. The originality of this approach lies in the parallel implementation of the learning algorithm for a model with an indefinite number of states and heterogeneous observed data: sometimes only the observed signal is available, and sometimes the state of the system is known. The code presented in this article are parallelized on several GPU devices.
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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 | 0.001 |
| 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