New Axiomatic Framework for Neural Networks Proposed
The Pursuit of Subspaces (PoS) hypothesis has been proposed by researchers as an axiomatic framework designed to interpret neural network behavior via geometric principles. This framework seeks to establish a comprehensive theoretical basis for the comprehension of representation, computation, and generalization within deep learning. It provides geometric insights into crucial aspects like representation structure and architectural functions. This research represents progress towards a cohesive theory, aiming to bridge the divide between practical achievements and theoretical insights.
Key facts
- The Pursuit of Subspaces (PoS) hypothesis is introduced as an axiomatic framework.
- It uses geometric postulates to describe neural network behavior.
- The framework addresses representation, computation, and generalization.
- It provides geometric explanations for deep learning phenomena.
- The work aims to bridge empirical performance and theoretical understanding.
- The paper is available on arXiv under computer science and machine learning.
Entities
Institutions
- arXiv