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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Romain Claret

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Conference Open access Aug 2026

Per-Node Activation Function Evolution in Indirectly Encoded Substrates: Solvability, Limits, and Emergent Diversity

Biological neurons achieve computational diversity through specialized types: tonic, bursting, adapting, and fast-spiking cells coexist within the same circuit. Artificial neural networks, by contrast, apply a single activation function uniformly to all nodes, which limits what they can represent. We show that this uni...

Romain Claret, Michael O’Neill, Paul Cotofrei et al. · 1 citation · ⚡1
#machine learning Preprint Aug 2026

On Scaling Coordinate-Based Neuroevolution: The Quadtree Bottleneck in ES-HyperNEAT

ES-HyperNEAT evolves substrate topology through adaptive quadtree subdivision; to our knowledge, no implementation with full population-level GPU parallelization exists. We present JAX-ESHN, a JAX-based implementation targeting GPU parallelization with batched CPPN queries, and benchmark it against the CPU-based PUREPL...

Romain Claret, Michael O'Neill, Paul Cotofrei et al. · 0 citations
#artificial intelligence Book Open access Jul 2024

Investigating Hyperparameter Optimization and Transferability for ES-HyperNEAT: A TPE Approach

This study investigates the optimization of ES-HyperNEAT hyperparameters using the Tree-structured Parzen Estimator (TPE) on the MNIST classification task, exploring a search space of over 3 billion potential combinations and provides insights into the hyperparameters' transferability across tasks of varying complexity...

Romain Claret, Michael O'Neill, Paul Cotofrei et al. · 5 citations · ⚡1
#artificial intelligence Book Open access Jul 2026

Tensor-Accelerated Eager Multi-Resolution Grids for Evolving Large-Scale Substrates

This work presents Eager Multi-Resolution HyperNEAT (EMR-HyperNEAT), which reformulates adaptive substrate discovery as a batch tensor operation: shared position grids are precomputed for all depths, every position is evaluated in one vectorized CPPN call, then the same variance criterion filters the output.

Romain Claret, Michael O'Neill, Paul Cotofrei et al. · 0 citations

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