Pattern and Anomaly Localization in Complex and Dynamic Data
Bibliographic record
Abstract
Following a series of deep learning breakthroughs in the area of image segmentation, multiple objects in an image input can be finely sub-categorized. Although Convolutional Neural Networks (CNNs) are known for their state-of-the-art performance in image classification, they present drawbacks when used to analyze different data types, such as time series. In this paper, we propose the Sequential Mask Convolutional Neural Network (SMCNN), a method that overcomes such drawbacks, and leverages CNNs for sequential data analysis. Our method transforms sequential data into an image representation by means of a specialized filter that produces flexible shape forms, and detects multiple types of outliers simultaneously. We evaluate the effectiveness of our method on data containing a variety of anomaly types combined with different concept drifts. The solution shows to significantly outperform prior endeavors and to provide high generalization capabilities on a wide array of data characteristics. We attribute its success to its ability to pinpoint the exact location of patterns and anomalies in parallel and to the invariance of CNNs, which allows them to adapt seamlessly to concept drifts.
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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".