Lightweight Tensor Deep Computation Model With Its Application in Intelligent Transportation Systems
Bibliographic record
Abstract
Deep computation models (DCMs) are widely used in intelligent transportation systems (ITS), like driving behavior detection, intelligent parking navigation and real-time road condition detection. Due to the multi-source heterogeneous nature of big data of the ITS, it is difficult for traditional DCMs to learn effective multi-modal data features. Although, the DCMs in tensor space can efficiently represent multi-modal data, it further worsens the problem of model learning parameter explosion. In this paper, we propose a lightweight tensor DCM. The model compresses the redundant learning parameters of the model and reduces the consumption of computational resources while maintaining the learning characterization capability of the DCM in tensor space, thus making the network model more general and lightweight for deploying the DCM to smart cars and edge devices. The proposed lightweight tensor DCM is evaluated on several real datasets. The experimental results show that the number of learning parameters is massively compressed while keeping the performance of the network model almost constant, while also reducing the computational complexity and training time of the model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".