Efficient learning of neighbor representations for boundary trees and\n forests
Bibliographic record
Abstract
We introduce a semiparametric approach to neighbor-based classification. We\nbuild off the recently proposed Boundary Trees algorithm by Mathy et al.(2015)\nwhich enables fast neighbor-based classification, regression and retrieval in\nlarge datasets. While boundary trees use an Euclidean measure of similarity,\nthe Differentiable Boundary Tree algorithm by Zoran et al.(2017) was introduced\nto learn low-dimensional representations of complex input data, on which\nsemantic similarity can be calculated to train boundary trees. As is pointed\nout by its authors, the differentiable boundary tree approach contains a few\nlimitations that prevents it from scaling to large datasets. In this paper, we\nintroduce Differentiable Boundary Sets, an algorithm that overcomes the\ncomputational issues of the differentiable boundary tree scheme and also\nimproves its classification accuracy and data representability. Our algorithm\nis efficiently implementable with existing tools and offers a significant\nreduction in training time. We test and compare the algorithms on the well\nknown MNIST handwritten digits dataset and the newer Fashion-MNIST dataset by\nXiao et al.(2017).\n
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".