IGANI: Iterative Generative Adversarial Networks for Imputation with\n Application to Traffic Data
Bibliographic record
Abstract
Increasing use of sensor data in intelligent transportation systems calls for\naccurate imputation algorithms that can enable reliable traffic management in\nthe occasional absence of data. As one of the effective imputation approaches,\ngenerative adversarial networks (GANs) are implicit generative models that can\nbe used for data imputation, which is formulated as an unsupervised learning\nproblem. This work introduces a novel iterative GAN architecture, called\nIterative Generative Adversarial Networks for Imputation (IGANI), for data\nimputation. IGANI imputes data in two steps and maintains the invertibility of\nthe generative imputer, which will be shown to be a sufficient condition for\nthe convergence of the proposed GAN-based imputation. The performance of our\nproposed method is evaluated on (1) the imputation of traffic speed data\ncollected in the city of Guangzhou in China, and the training of short-term\ntraffic prediction models using imputed data, and (2) the imputation of\nmulti-variable traffic data of highways in Portland-Vancouver metropolitan\nregion which includes volume, occupancy, and speed with different missing rates\nfor each of them. It is shown that our proposed algorithm mostly produces more\naccurate results compared to those of previous GAN-based imputation\narchitectures.\n
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".