<ul data-eligibleForWebStory="true"><li>Contextual Self-Modulation (CSM) is a regularization mechanism for Neural Context Flows (NCFs) known for powerful meta-learning on physical systems.</li><li>CSM has limitations across different modalities and in high-data regimes.</li><li>Two extensions have been introduced in this work: iCSM, which expands CSM to infinite-dimensional variations, and StochasticNCF, which provides a low-cost approximation of meta-gradient updates.</li><li>The extensions were tested on tasks such as dynamical systems, computer vision challenges, and curve fitting problems.</li><li>Incorporating higher-order Taylor expansions showed that they do not necessarily improve generalization.</li><li>CSM can be integrated into other meta-learning frameworks with FlashCAVIA.</li><li>The study emphasizes the benefits of CSM for meta-learning and out-of-distribution tasks, particularly suited for physical systems.</li><li>An open-source library for integrating self-modulation into contextual meta-learning workflows is available at https://github.com/ddrous/self-mod.</li></ul>

Reevaluating Meta-Learning Optimization Algorithms Through Contextual Self-Modulation

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