Generative Adversarial Networks as an Accommodative Memory for Cognitive Waveform Synthesis
Bibliographic record
Abstract
This paper presents a practical example where generative adversarial networks (GANs) can be employed as an accommodative memory unit (AMU). An array of such units can memorize/learn any algorithm’s results. This kind of memory can accommodate their response to new unseen scenarios by traversing the GAN’s latent space and finding the best answer. Accordingly, accommodative memory (AM) can be viewed as a generalization of look-up tables (LUT), in which writing and reading operations are equivalent to training and inference of an AMU or traversing its latent space. We explore cognitive radar waveform synthesis to showcase a practical application of the proposed AM concept. In this regard, a Wasserstein GAN (WGAN) is trained as an AMU for a particular ambiguity function (AF) shaping scenario. Here, retrieving information for the most frequent scenarios, called input basis scenarios (IBSs), involves only the inference of the generator. For more complicated input scenarios, the memory accommodates the input by traversing the latent space using ADAM optimization. Compared to redesigning the AF, the AM can remember or accommodate new scenarios several orders of magnitude faster at the expense of more memory hardware. As an auxiliary result, we also demonstrate that traditional algorithms can be defeated in terms of suppression level by penalizing the loss function according to desired AF.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".