arXiv:2601.11334v2 Announce Type: replace-cross Abstract: An information-theoretic framework is introduced to analyze last-layer embedding, focusing on learned representations for regression tasks. We define representation-rate and derive limits on the reliability with which input-output information can be represented as is inherently determined by the input-source entropy. We further define representation capacity in a perturbed setting, and representation rate-distortion for a compressed output. We derive the achievable capacity, the achievable representation-rate, and their converse. Finall
The proliferation of AI systems necessitates a deeper theoretical understanding of representation learning to improve efficiency, robustness, and interpretability, which is amplified by growing industrial and academic investment.
This research provides fundamental theoretical limits and frameworks for AI representation learning, which underpins the efficiency and capabilities of future AI models across various applications.
A more rigorous theoretical foundation for understanding and designing efficient AI representation learning algorithms, potentially leading to more robust and less data-intensive AI systems.
- · AI researchers
- · Machine learning engineers
- · Data scientists
- · AI hardware developers
- · Inefficient AI models
- · Companies relying on brute-force AI development
Improved efficiency and robustness of AI models, especially for complex tasks.
Reduced computational resource requirements for certain AI training and deployment, indirectly impacting the energy consumption and compute supply chain.
Acceleration of advanced AI applications in critical sectors by making models more reliable and interpretable, potentially bolstering the development of agentic AI systems.
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Read at arXiv cs.LG