The Mating Rituals of Deep Neural Networks: Learning Compact Feature\n Representations through Sexual Evolutionary Synthesis
Bibliographic record
Abstract
Evolutionary deep intelligence was recently proposed as a method for\nachieving highly efficient deep neural network architectures over successive\ngenerations. Drawing inspiration from nature, we propose the incorporation of\nsexual evolutionary synthesis. Rather than the current asexual synthesis of\nnetworks, we aim to produce more compact feature representations by\nsynthesizing more diverse and generalizable offspring networks in subsequent\ngenerations via the combination of two parent networks. Experimental results\nwere obtained using the MNIST and CIFAR-10 datasets, and showed improved\narchitectural efficiency and comparable testing accuracy relative to the\nbaseline asexual evolutionary neural networks. In particular, the network\nsynthesized via sexual evolutionary synthesis for MNIST had approximately\ndouble the architectural efficiency (cluster efficiency of 34.29X and synaptic\nefficiency of 258.37X) in comparison to the network synthesized via asexual\nevolutionary synthesis, with both networks achieving a testing accuracy of\n~97%.\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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".