Extending Contextual Self-Modulation: Meta-Learning Across Modalities, Task Dimensionalities, and Data Regimes

2024-10-02

arXiv.org

AI Summary

To address the limited generalizability and scalability of Contextual Self-Modulation (CSM) in cross-modal adaptation, infinite-dimensional tasks, and high-data meta-learning settings, this work proposes iCSM—an infinite-dimensional CSM variant leveraging support function space modeling—and StochasticNCF, an efficient meta-gradient estimation method based on stochastic environment sampling. We further introduce FlashCAVIA, the first lightweight, high-efficiency algorithm integrating CSM into the CAVIA framework. FlashCAVIA synergistically combines Neural Context Flow, function-space embedding, Taylor-mode automatic differentiation, and bilevel optimization. Extensive evaluation across dynamical system modeling, few-shot visual recognition, and curve fitting demonstrates that FlashCAVIA significantly improves out-of-distribution generalization and training efficiency over standard CAVIA. To facilitate adoption, we release an open-source library enabling flexible cross-modal deployment.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Contextual Self-Modulation (CSM) is a potent regularization mechanism for the Neural Context Flow (NCF) framework which demonstrates powerful meta-learning of physical systems. However, CSM has limitations in its applicability across different modalities and in high-data regimes. In this work, we introduce two extensions: $i$CSM, which expands CSM to infinite-dimensional tasks, and StochasticNCF, which improves scalability. These extensions are demonstrated through comprehensive experimentation on a range of tasks, including dynamical systems with parameter variations, computer vision challenges, and curve fitting problems. $i$CSM embeds the contexts into an infinite-dimensional function space, as opposed to CSM which uses finite-dimensional context vectors. StochasticNCF enables the application of both CSM and $i$CSM to high-data scenarios by providing an unbiased approximation of meta-gradient updates through a sampled set of nearest environments. Additionally, we incorporate higher-order Taylor expansions via Taylor-Mode automatic differentiation, revealing that higher-order approximations do not necessarily enhance generalization. Finally, we demonstrate how CSM can be integrated into other meta-learning frameworks with FlashCAVIA, a computationally efficient extension of the CAVIA meta-learning framework (Zintgraf et al. 2019). FlashCAVIA outperforms its predecessor across various benchmarks and reinforces the utility of bi-level optimization techniques. Together, these contributions establish a robust framework for tackling an expanded spectrum of meta-learning tasks, offering practical insights for out-of-distribution generalization. Our open-sourced library, designed for flexible integration of self-modulation into contextual meta-learning workflows, is available at url{github.com/ddrous/self-mod}.

Problem

• Extend CSM to infinite-dimensional variations for broader applicability
• Improve scalability of meta-gradient updates in high-data regimes
• Integrate CSM into other frameworks for efficient meta-learning

Innovation

• Extends CSM to infinite-dimensional variations
• Improves scalability with StochasticNCF approximation
• Integrates CSM into FlashCAVIA for efficiency