1 citations · 2 across the 11 of their papers we have counts for
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MetaLearnNCA: Few-Shot Offline Meta-Learning via Interacting Neural Cellular Automata
Etienne Guichard, Stefano Nichele
Few-shot meta-learning traditionally formulates task adaptation either as analytical gradient descent through unrolled computational graphs or as metric-based distance comparisons…
Benchmarking the Connectomes of Caenorhabditis elegans within the Reservoir Computing Framework
Felix S. Reimers, Ola Huse Ramstad, Aliaksandr Hubin +1
The aim of this work is to examine the connectomes of Caenorhabditis elegans through a computational lens using the reservoir computing framework. Connectomes are mappings of biolo…
Neural Cellular Automata Learn General Features in their Hidden Channels
Etienne Guichard, Stefano Nichele
Modern deep learning models achieve impressive generalization through over-parameterization, but this paradigm often struggles with overfitting and memorization in few-shot regimes…
Exploring napping paradigm for Recurrent Spiking Neural Networks
Andreas Massey, Stefano Nichele, Aliaksandr Hubin
Biological organisms minimize free energy by balancing two competing demands on their internal world model: it must be accurate enough to predict sensory input, yet simple enough t…
Benchmarking the State of Networks with a Low-Cost Method Based on Reservoir Computing
Felix Simon Reimers, Carl-Hendrik Peters, Stefano Nichele
Using data from mobile network utilization in Norway, we showcase the possibility of monitoring the state of communication and mobility networks with a non-invasive, low-cost metho…