Bibliographic record
Abstract
Neural networks have been widely utilized for wireless communication optimizations. In most of the literature, a dedicated neural network is trained for each specific optimization problem. However, under many scenarios, several distinct objectives are worth optimizing on the same wireless environment. Instead of exhaustively training a new model for every objective, it is more efficient to exploit the correlations between these objectives to train models with shared model parameters and feature representations. In the deep learning literature, transfer learning has been proposed to encourage knowledge transfer among models solving correlated problems. Unlike a majority of transfer learning applications where the high level features are relatively easy to locate in the neural networks, this paper considers wireless communication problems, in which it is much more difficult to identify high level features transferable to correlated tasks. To address this issue, this paper proposes to add an additional reconstruction loss when training the model. This new loss is for reconstructing the problem inputs starting from a selected neural network hidden layer. This approach encourages the features learnt to be general and descriptive about the inputs, instead of being solely responsible for minimizing the specific task-based loss. When a new objective is to be optimized, these features can be readily used for transfer learning. Simulation results in device-to-device wireless network power allocation optimization suggest that the proposed approach is highly efficient in data and model complexity, resilient to over-fitting, and supports competitive optimization performances.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".