A Highly Optimized GPU Batched Elasticnet Solver (BENS) with Application to Real- Time Keypoint Detection for Image Retrieval
Bibliographic record
Abstract
In this paper, we present a highly optimized GPU batched elastic-net solver (BENS) with application to real-time key-point detection for image retrieval. BENS was optimized to perform hundreds of thousands of small elastic-net fits by batching each fit from specific steps in the elastic-net computation into a large matrix multiplication which can be computed efficiently using the CUBLAS library. The main motivation for BENS was a real-time implementation of the Sparse-Coding Key-point detector (SCK) algorithm which has reaching applications in science, engineering, social science and medicine. When BENS was applied to accelerate SCK, we have achieved a 232x speed up compared to the original CPU implementation of SCK. To demonstrate the newly accelerated SCK algorithm, we conducted an Bo Vw based image retrieval experiment using SCK as the key-point detector.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".