An Adaptive Rank-Based Tensor Ring Completion Model for Intelligent Transportation Systems
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
Low rank tensor ring based data recovery algorithms have been widely used in vehicle road cooperation intelligent transportation system to recover missing data entries in the sensing data pre-processing stage. However, the existing tensor ring decomposition based methods often resolve the low rank optimization with predefined ranks, which often leads over-fitting when the selected rank is large. To overcome this challenge, we propose a Bayesian inference based tensor ring completion method which can automatically learn an optimal rank for the tensor ring completion. In this work, a likelihood Statistical model is firstly developed for low rank tensor ring approximation, and we impose a hierarchical sparse induced prior on the forward and horizontal slices of the kernel factor. Then, the Variational Bayesian algorithm is used to derive the parameters in the model, and the tensor ring rank can be achieved by gradually pruning the sparse horizontal and forward slice components in the factor. Finally, to elevate the proposal, numerous experiments have been conducted on two different intelligent transportation datasets, and the experimental results show that the proposed method can get the state-of-the-art performance in terms of recovery accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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