Generative Adversarial Networks as an Accommodative Memory for Cognitive Waveform Synthesis
Bibliographic record
Abstract
This paper introduces a new concept of Generative Accommodative Memory (GAM) by showcasing a practical example of using Generative Adversarial Networks (GANs) as Accommodative Memory Basic Units (AMBUs). The GAM can memorize and learn the results of any algorithm and adapt its response to new unseen scenarios by exploring the latent space. This memory is a generalization of look-up tables (LUT), where writing and reading operations correspond to the training and inference of an AMBU or traversing its latent space. To demonstrate the practical application of GAM, we use it in cognitive radar waveform synthesis. Here, a Wasserstein GAN is trained as an AMBU for a specific ambiguity function shaping scenario. The memory can retrieve information for frequent basic scenarios (called input basis scenarios) through the inference of the generator, i.e., generative read. For more complex inputs, the memory accommodates the input by optimizing output over the latent space, i.e., accommodation read. In this light, the GAM can accommodate new scenarios much faster than traditional methods, but at the cost of more memory hardware. As an additional result, we show that traditional algorithms can be outperformed in terms of suppression level by penalizing the loss function according to the desired ambiguity function.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".