resnet-18-c7d69784·2 events·first seen Aliases: ResNet-18, ResNet18
NeuronSoup is a neural computation architecture that replaces synchronous layer-by-layer processing with asynchronous, delay-mediated signal propagation through a pool of physically shared neurons, optimized entirely by a genetic algorithm rather than gradient descent. On MNIST digit classification using frozen ResNet18 features, the evolved network achieves 85.9% test accuracy with 204 active paths through 266 hidden neurons, fitting in 115 KB. The architecture requires no differentiable computation graph, adapts computation depth per sample, and discovers lateral pathway interactions without explicit engineering. The authors argue genetic algorithms are the appropriate optimizer for this problem class and discuss why CMA-ES fails at this scale.
A new arXiv preprint investigates the geometric structure of recoverability in continual learning using Split CIFAR-100 and a sequentially trained ResNet-18. The authors introduce Recovery Subspace Dimensionality (k_t) and find that recovery dimensionality remains stable across tasks (mean k_t = 8.0) despite substantial representational drift, with principal-angle drift strongly predicting recoverability (r = -0.862). The findings support the Stable Recovery Manifold hypothesis: forgotten knowledge remains compactly decodable, reframing catastrophic forgetting as a manifold-alignment and accessibility problem rather than true information loss.