Error Bounds for Online Predictions of Linear-Chain Conditional Random Fields: Application to Activity Recognition for Users of Rolling Walkers
Bibliographic record
Abstract
Linear-Chain Conditional Random Fields (L-CRFs) are a versatile class of models for the distribution of a sequence of hidden states ("labels") conditional on a sequence of observable variables. In general, the exact conditional marginal distributions of the labels can be computed only after the complete sequence of observations has been obtained, which forbids the prediction of labels in an online fashion. This paper considers approximations of the marginal distributions which only take into account past observations and a small number of observations in the future. Based on these approximations, labels can be predicted close to real-time. We establish rigorous bounds for the marginal distributions which can be used to assess the approximation error at runtime. We apply the results to an L-CRF which recognizes the activity of rolling walker users from a stream of sensor data. It turns out that if we allow for a prediction delay of half of a second, the online predictions achieve almost the same accuracy as the offline predictions based on the complete observation sequences.
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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 | 0.000 | 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 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".