A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS

• URL: http://arxiv.org/abs/2004.01899v3
• Date: Tue, 1 Sep 2020 01:06:51 GMT
• Title: A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS
• Authors: Xuefei Ning, Yin Zheng, Tianchen Zhao, Yu Wang, and Huazhong Yang
• Abstract summary: This work proposes a novel Graph-based neural ArchiTecture Scheme, a.k.a. a GATES, to improve the predictor-based neural architecture search. Gates models the operations as the transformation of the propagating information, which mimics the actual data processing of neural architecture.
• Score: 18.409809742204896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work proposes a novel Graph-based neural ArchiTecture Encoding Scheme, a.k.a. GATES, to improve the predictor-based neural architecture search. Specifically, different from existing graph-based schemes, GATES models the operations as the transformation of the propagating information, which mimics the actual data processing of neural architecture. GATES is a more reasonable modeling of the neural architectures, and can encode architectures from both the "operation on node" and "operation on edge" cell search spaces consistently. Experimental results on various search spaces confirm GATES's effectiveness in improving the performance predictor. Furthermore, equipped with the improved performance predictor, the sample efficiency of the predictor-based neural architecture search (NAS) flow is boosted. Codes are available at https://github.com/walkerning/aw_nas.

Related papers

• Knowledge-aware Evolutionary Graph Neural Architecture Search [49.13787973318586]
  Graph neural architecture search (GNAS) can customize high-performance graph neural network architectures for specific graph tasks or datasets. Existing GNAS methods begin searching for architectures from a zero-knowledge state, ignoring the prior knowledge that may improve the search efficiency. This study proposes exploiting such prior knowledge to accelerate the multi-objective evolutionary search on a new graph dataset.
  arXiv  Detail & Related papers  (2024-11-26T11:32:45Z)
• FR-NAS: Forward-and-Reverse Graph Predictor for Efficient Neural Architecture Search [10.699485270006601]
  We introduce a novel Graph Neural Networks (GNN) predictor for Neural Architecture Search (NAS) This predictor renders neural architectures into vector representations by combining both the conventional and inverse graph views. The experimental results showcase a significant improvement in prediction accuracy, with a 3%--16% increase in Kendall-tau correlation.
  arXiv  Detail & Related papers  (2024-04-24T03:22:49Z)
• GeNAS: Neural Architecture Search with Better Generalization [14.92869716323226]
  Recent neural architecture search (NAS) approaches rely on validation loss or accuracy to find the superior network for the target data. In this paper, we investigate a new neural architecture search measure for excavating architectures with better generalization.
  arXiv  Detail & Related papers  (2023-05-15T12:44:54Z)
• NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction [37.357949900603295]
  We propose a neural architecture representation model that can be used to estimate attributes holistically. Experiment results show that our proposed framework can be used to predict the latency and accuracy attributes of both cell architectures and whole deep neural networks.
  arXiv  Detail & Related papers  (2022-11-15T10:15:21Z)
• Network Graph Based Neural Architecture Search [57.78724765340237]
  We search neural network by rewiring the corresponding graph and predict the architecture performance by graph properties. Because we do not perform machine learning over the entire graph space, the searching process is remarkably efficient.
  arXiv  Detail & Related papers  (2021-12-15T00:12:03Z)
• Edge-featured Graph Neural Architecture Search [131.4361207769865]
  We propose Edge-featured Graph Neural Architecture Search to find the optimal GNN architecture. Specifically, we design rich entity and edge updating operations to learn high-order representations. We show EGNAS can search better GNNs with higher performance than current state-of-the-art human-designed and searched-based GNNs.
  arXiv  Detail & Related papers  (2021-09-03T07:53:18Z)
• Rethinking Graph Neural Network Search from Message-passing [120.62373472087651]
  This paper proposes Graph Neural Architecture Search (GNAS) with novel-designed search space. We design Graph Neural Architecture Paradigm (GAP) with tree-topology computation procedure and two types of fine-grained atomic operations. Experiments show that our GNAS can search for better GNNs with multiple message-passing mechanisms and optimal message-passing depth.
  arXiv  Detail & Related papers  (2021-03-26T06:10:41Z)
• Neural Architecture Search based on Cartesian Genetic Programming Coding Method [6.519170476143571]
  We propose an evolutionary approach of NAS based on CGP, called CGPNAS, to solve sentence classification task. The experimental results show that the searched architectures are comparable with the performance of human-designed architectures.
  arXiv  Detail & Related papers  (2021-03-12T09:51:03Z)
• A Semi-Supervised Assessor of Neural Architectures [157.76189339451565]
  We employ an auto-encoder to discover meaningful representations of neural architectures. A graph convolutional neural network is introduced to predict the performance of architectures.
  arXiv  Detail & Related papers  (2020-05-14T09:02:33Z)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.