Deep Computation Model for Unsupervised Feature Learning on Big Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deep learning has been successfully applied to feature learning in speech recognition, image classification and language processing. However, current deep learning models work in the vector space, resulting in the failure to learn features for big data since a vector cannot model the highly non-linear distribution of big data, especially heterogeneous data. This paper proposes a deep computation model for feature learning on big data, which uses a tensor to model the complex correlations of heterogeneous data. To fully learn the underlying data distribution, the proposed model uses the tensor distance as the average sum-of-squares error term of the reconstruction error in the output layer. To train the parameters of the proposed model, the paper designs a high-order back-propagation algorithm (HBP) by extending the conventional back-propagation algorithm from the vector space to the high-order tensor space. To evaluate the performance of the proposed model, we carried out the experiments on four representative datasets by comparison with stacking auto-encoders and multimodal deep learning models. Experimental results clearly demonstrate that the proposed model is efficient to perform feature learning when evaluated using the STL-10, CUAVE, SANE and INEX datasets.
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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 | 0.000 |
| 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