Efficient learning of neighbor representations for boundary trees and\n forests
Why this work is in the frame
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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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it