Using Knowledge to Speed Learning: A Comparison of Knowledge-based Cascade-correlation and Multi-task Learning
Bibliographic record
Abstract
Cognitive modeling with neural networks unrealistically ignores the role of knowledge in learning by starting from random weights. It is likely that effective use of knowledge by neural networks could significantly speed learning. A new algorithm, knowledge-based cascadecorrelation (KBCC), finds and adapts its relevant knowledge in new learning. Comparison to multi-task learning (MTL) reveals that KBCC uses its knowledge more effectively to learn faster. 1. Existing Knowledge and New Learning Neural networks typically learn de novo without the benefit of existing knowledge. However, when people learn, they routinely use their knowledge (Pazzani, 1991; Wisniewski, 1995). Such use of prior knowledge in learning is likely responsible for the ease and speed with which people learn, and for interference with new learning. The technical reason that neural networks fail to use knowledge is that they begin learning from initially random connection weights. This implements a tabula rasa view of each distinct learning task that very few cognitive psychologists would accept. In this paper, we compare two algorithms (KBCC and MTL) for their ability to use knowledge to speed learning. KBCC is an extension of cascade-correlation (CC), a generative learning algorithm often used in the simulation of cognitive development (Buckingham & Shultz, in
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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 | 0.013 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".