Bibliographic record
Abstract
The traditional machine learning models can only learn from stationary data stream and are very vulnerable to the changing environment.To encourage the machine learners to be more human-like and practical in the real-world, continual learning was put forward and attracts a surge of attention in recent few years.Conventionally, there are three scenarios in continual learning which are class-incremental, taskincremental and domain-incremental and each scenario is associated with a specific model configuration.The most critical problem addressed by continual learning is catastrophic forgetting on previously learned tasks, whereupon this thesis aims to tackle this problem for classification tasks in different continual learning settings.Towards this end, this thesis first proposes a continual learning approach with dual regularizations on output and representation spaces for domain-incremental learning.The output space is regularized by means of knowledge distillation to preserve acquired information from old tasks and the representational regularization resorts to feature selection via sparse regularizer so that the unique classifier is able to perform well in the filtered feature space.The second approach is an online continual learning algorithm applicable to all three continual learning scenarios.Specifically, the feature propagation from previous feature space to current one is employed to retain some past knowledge and a contrastive regularization loss to prevent the feature space i Dr.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".