Sparse image reconstruction by two phase RBM learning: Application to mine planning
Why this work is in the frame
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Bibliographic record
Abstract
A key problem in mine planning is estimating the locations of underground ore bodies from a set of sparse core samples that span the area to be excavated. Data from each sample location are interpreted by a geologist and rendered as an image depicting the local ore distribution. The goal is to reconstruct these sparse samples into a dense image that can correctly account for the underground structure. From a computer vision perspective, this has the form of a sparse data reconstruction problem, and is often tackled using a stochastic reconstruction approach. However in the present case the nature of the data is such that most conventional approaches fall short. In this paper we introduce a stochastic reconstruction method that uses a Restricted Boltzmann Machine (RBM) architecture to solve the problem in a novel way. Specifically, it incorporates a two-phase learning approach that i) uses dense sample information available from already excavated areas of the mine to build a general appearance model, and then ii) conditions this model to account for the data in the core sample images. Reconstruction is then accomplished by sampling the distribution implicit in the in the RBM after learning. Our results show that this approach offers significant improvements to conventional stochastic reconstruction algorithms as the RBM is better able to learn the distribution underlying the sample data.
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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 | 0.000 | 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 | 0.001 |
| 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 it