Experiences with Contrastive Predictive Coding in Industrial Time-Series Classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multivariate time-series classification problems are found in many industrial settings; for example, fault detection in a manufacturing process by monitoring sensors signals. It is difficult to obtain large labeled datasets in these settings, for reasons such as limitations in the automatic recording, the need for expert root-cause analysis, and the very limited access to human experts. Therefore, methods that perform classification in a label efficient manner are useful for building and deploying machine learning models in the industrial setting. In this work, we apply a self-supervised learning method called Contrastive Predictive Coding (CPC) to classification tasks on three industrial multivariate time-series datasets. First, the CPC neural network (CPC base) is trained with a large number of unlabeled time-series data instances. Then, a standard supervised classifier such as a multi-layer perception (MLP) is trained on available labeled data using the output embeddings from the pre-trained CPC base. On all three classification datasets, we see increased label efficiency (ability to reach a goal accuracy level with less labeled examples). In the low data regime (10's or few 100's of labeled examples), the CPC pre-trained model achieves high accuracy with up to 15x less labels than a model trained only on labeled data. We also conduct experiments to evaluate the usefulness of CPC pre-trained classifiers as base models to start an active learning loop, and find that uncertainty sampling does not perform significantly better than random sampling during the initial queries.
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.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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