| _version_ | 1866901382452215808 |
|---|---|
| author | Revista, Zen IA, 10 |
| author_facet | Revista, Zen IA, 10 |
| contents | This paper introduces a novel unified learning framework designed to adaptively manage information granularity for both sparse and dense representations. Traditional machine learning paradigms often compartmentalize sparse and dense representation learning, leading to suboptimal performance in tasks that inherently require a dynamic balance between fine-grained detail and high-level abstraction. Our proposed framework, termed Adaptive Information Granularity (AIG), addresses this limitation by integrating mechanisms that dynamically adjust the level of information granularity based on the input data characteristics, task requirements, and computational constraints. The AIG framework employs a multi-stage architecture comprising an initial feature embedding module, a granularity modulation layer capable of inducing either sparsity or density, and an adaptive objective function that balances representation fidelity with desired sparsity/density levels. We detail the theoretical underpinnings of adaptive granularity, drawing connections to concepts from rough set theory and fuzzy systems, and present a practical implementation leveraging deep learning architectures. Through empirical evaluations, we demonstrate that AIG can effectively learn context-aware representations, outperforming methods that adhere strictly to either sparse or dense paradigms across various machine learning tasks, including text classification, image recognition, and recommendation systems. The framework's ability to seamlessly transition between different granularities offers significant advantages in interpretability, computational efficiency, and generalization capabilities, paving the way for more robust and versatile AI systems. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17817553 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Adaptive Information Granularity: A Unified Learning Framework for Sparse and Dense Representations Revista, Zen IA, 10 This paper introduces a novel unified learning framework designed to adaptively manage information granularity for both sparse and dense representations. Traditional machine learning paradigms often compartmentalize sparse and dense representation learning, leading to suboptimal performance in tasks that inherently require a dynamic balance between fine-grained detail and high-level abstraction. Our proposed framework, termed Adaptive Information Granularity (AIG), addresses this limitation by integrating mechanisms that dynamically adjust the level of information granularity based on the input data characteristics, task requirements, and computational constraints. The AIG framework employs a multi-stage architecture comprising an initial feature embedding module, a granularity modulation layer capable of inducing either sparsity or density, and an adaptive objective function that balances representation fidelity with desired sparsity/density levels. We detail the theoretical underpinnings of adaptive granularity, drawing connections to concepts from rough set theory and fuzzy systems, and present a practical implementation leveraging deep learning architectures. Through empirical evaluations, we demonstrate that AIG can effectively learn context-aware representations, outperforming methods that adhere strictly to either sparse or dense paradigms across various machine learning tasks, including text classification, image recognition, and recommendation systems. The framework's ability to seamlessly transition between different granularities offers significant advantages in interpretability, computational efficiency, and generalization capabilities, paving the way for more robust and versatile AI systems. |
| title | Adaptive Information Granularity: A Unified Learning Framework for Sparse and Dense Representations |
| url | https://doi.org/10.5281/zenodo.17817553 |