higherrep

HigherRep is a hypothetical open-source software framework designed to enable researchers and developers to work with higher-order representations in machine learning and data science. The project defines a set of abstractions for hierarchical, compositional, and cross-modal representations and provides tooling to create, train, and evaluate models that operate on these representations.

Design goals for HigherRep include interoperability with major ML libraries, support for higher-order tensors and category-theoretic constructs, and a plugin architecture that allows backends, datasets, and evaluation metrics to be extended without changing core code. The framework emphasizes modularity and reproducibility, aiming to lower the barrier to experimenting with advanced representation structures.

Architecture and core concepts centers on higher-order representation objects that can be composed via flexible mappings and pipelines that propagate representations through multiple processing stages. The framework typically includes modules for data preprocessing, model construction, training loops, evaluation, and visualization. A modular plugin system enables researchers to swap components such as encoders, decoders, and reasoning modules while maintaining a consistent interface.

Applications of HigherRep span natural language processing, computer vision, graph representation learning, and multimodal tasks that require hierarchical reasoning and abstraction. It serves as a platform for exploring ideas in higher-order representation learning, including multi-scale features, compositional generalization, and cross-domain reasoning.

Development and licensing are community-driven, with public repositories and documentation. The project commonly adopts an open-source license and a governance model based on contributor roles and maintainers. Clear documentation, tutorials, and example notebooks are emphasized to support learning and collaboration. See also higher representation theory, representation learning, and tensor networks.