← Back to all news
arXiv AI By Etienne Guichard, Stefano Nichele

MetaLearnNCA: Few-Shot Offline Meta-Learning via Interacting Neural Cellular Automata

Read the original on arXiv AI →

MetaLearnNCA introduces a decentralized few‑shot meta‑learning framework that uses interacting Neural Cellular Automata (NCAs) to adapt to new tasks without backpropagation. The method splits adaptation into an Active‑NCA that performs task inference on a 2D spatial memory grid, and a learned Meta‑NCA that diffuses error residuals locally to update this grid. Experiments show competitive in‑distribution performance (96.12% on Omniglot) and superior out‑of‑distribution transfer on MNIST, KMNIST, and Fashion‑MNIST compared to canonical meta‑learners.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Sep 21

Neural Cellular Automata Learn General Features in their Hidden Channels

Neural Cellular Automata (NCAs) are shown to learn general, scale‑invariant topological primitives in their hidden channels, which can be transferred from a teacher to a student model for few‑shot learning. The study introduces a transfer‑learning mechanism that injects pretrained hidden states into a student, improving early optimization and outperforming recurrent and feed‑forward baselines on MNIST benchmarks with only ~9,800 parameters. Mechanistic analysis reveals that hidden channels decouple feature extraction from classification, converging to mutually orthogonal states that absorb morphological complexity.

By Etienne Guichard, Stefano Nichele
fine-tuningbenchmarks
More like this →
arXiv AI
Jul 10

Architecture Generalization with MetaNCA

arXiv:2607. 07743v1 Announce Type: cross Abstract: Self-organization is an emergent property of life, driven by the collective behavior of individual components acting on local information.

By Meet Barot, Daniel Berenberg, Sina Khajehabdollahi
llms
More like this →
arXiv Machine Learning
Sep 25

Online Task Adaptation via Self-Organisation

The paper proposes a method for task adaptation that eliminates the need for gradient computation during adaptation. Using a Neural Cellular Automaton, the authors train recurrent dynamics and memory read/write operations via backpropagation, then fix the slow model parameters. Online adaptation is achieved solely through local memory updates driven by prediction errors, enabling significant performance gains on new classification tasks with a single support set pass.

By Krsto Prorokovi\'c
More like this →