Weight-Entanglement Meets Gradient-Based Neural Architecture Search

• URL: http://arxiv.org/abs/2312.10440v1
• Date: Sat, 16 Dec 2023 13:15:44 GMT
• Title: Weight-Entanglement Meets Gradient-Based Neural Architecture Search
• Authors: Rhea Sanjay Sukthanker, Arjun Krishnakumar, Mahmoud Safari, Frank Hutter
• Abstract summary: Weight sharing is a fundamental concept in neural architecture search (NAS) Weight emphentanglement has emerged as a technique for intricate parameter sharing among architectures within macro-level search spaces. Blackbox optimization methods have been commonly employed, particularly in conjunction with supernet training, to maintain search efficiency. This paper proposes a novel scheme to adapt gradient-based methods for weight-entangled spaces.
• Score: 44.655931666517645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weight sharing is a fundamental concept in neural architecture search (NAS), enabling gradient-based methods to explore cell-based architecture spaces significantly faster than traditional blackbox approaches. In parallel, weight \emph{entanglement} has emerged as a technique for intricate parameter sharing among architectures within macro-level search spaces. %However, the macro structure of such spaces poses compatibility challenges for gradient-based NAS methods. %As a result, blackbox optimization methods have been commonly employed, particularly in conjunction with supernet training, to maintain search efficiency. %Due to the inherent differences in the structure of these search spaces, these Since weight-entanglement poses compatibility challenges for gradient-based NAS methods, these two paradigms have largely developed independently in parallel sub-communities. This paper aims to bridge the gap between these sub-communities by proposing a novel scheme to adapt gradient-based methods for weight-entangled spaces. This enables us to conduct an in-depth comparative assessment and analysis of the performance of gradient-based NAS in weight-entangled search spaces. Our findings reveal that this integration of weight-entanglement and gradient-based NAS brings forth the various benefits of gradient-based methods (enhanced performance, improved supernet training properties and superior any-time performance), while preserving the memory efficiency of weight-entangled spaces. The code for our work is openly accessible \href{https://anonymous.4open.science/r/TangleNAS-527C}{here}

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