A Measure of Relatedness for Selecting Consolidated Task Knowledge.
Bibliographic record
Abstract
The selective transfer of task knowledge is studied within the context of multiple task learning (MTL) neural networks. Given a consolidated MTL network of previously learned tasks and a new primary task, T0, a measure of task relatedness is derived. The existing consolidated MTL network representation is xed and an output for task T0 is connected to the hidden nodes of the network and trained. The cosine similarity be-tween the hidden to output weight vectors for T0 and the weight vectors for each of the previously learned tasks is used as measure of task relatedness. The most re-lated tasks are then used to learn T0 within a new MTL network using the task rehearsal method. Results of an empirical study on two synthetic domains of invariant concept tasks demonstrate the method's ability to selec-tively transfer knowledge from the most related tasks so as to develop hypotheses with superior generalization.
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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".