Visual Analysis of Neural Architecture Spaces for Summarizing Design Principles

• URL: http://arxiv.org/abs/2208.09665v1
• Date: Sat, 20 Aug 2022 12:15:59 GMT
• Title: Visual Analysis of Neural Architecture Spaces for Summarizing Design Principles
• Authors: Jun Yuan, Mengchen Liu, Fengyuan Tian, and Shixia Liu
• Abstract summary: ArchExplorer is a visual analysis method for understanding a neural architecture space and summarizing design principles. A circle-packing-based architecture visualization has been developed to convey both the global relationships between clusters and local neighborhoods of the architectures in each cluster. Two case studies and a post-analysis are presented to demonstrate the effectiveness of ArchExplorer in summarizing design principles and selecting better-performing architectures.
• Score: 22.66053583920441
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in artificial intelligence largely benefit from better neural network architectures. These architectures are a product of a costly process of trial-and-error. To ease this process, we develop ArchExplorer, a visual analysis method for understanding a neural architecture space and summarizing design principles. The key idea behind our method is to make the architecture space explainable by exploiting structural distances between architectures. We formulate the pairwise distance calculation as solving an all-pairs shortest path problem. To improve efficiency, we decompose this problem into a set of single-source shortest path problems. The time complexity is reduced from O(kn^2N) to O(knN). Architectures are hierarchically clustered according to the distances between them. A circle-packing-based architecture visualization has been developed to convey both the global relationships between clusters and local neighborhoods of the architectures in each cluster. Two case studies and a post-analysis are presented to demonstrate the effectiveness of ArchExplorer in summarizing design principles and selecting better-performing architectures.

Related papers

• EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition [54.99121380536659]
  Eye movement biometrics have received increasing attention thanks to its high secure identification. Deep learning (DL) models have been recently successfully applied for eye movement recognition. DL architecture still is determined by human prior knowledge. We propose EM-DARTS, a hierarchical differentiable architecture search algorithm to automatically design the DL architecture for eye movement recognition.
  arXiv  Detail & Related papers  (2024-09-22T13:11:08Z)
• Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision [51.88848982611515]
  Unsupervised graph neural architecture search remains unexplored in the literature. We propose a novel Disentangled Self-supervised Graph Neural Architecture Search model. Our model is able to achieve state-of-the-art performance against several baseline methods in an unsupervised manner.
  arXiv  Detail & Related papers  (2024-03-08T05:23:55Z)
• Neural Architecture Retrieval [27.063268631346713]
  We define a new problem Neural Architecture Retrieval which retrieves a set of existing neural architectures with similar designs to the query neural architecture. Existing graph pre-training strategies cannot address the computational graph in neural architectures due to the graph size and motifs. We introduce multi-level contrastive learning to achieve accurate graph representation learning.
  arXiv  Detail & Related papers  (2023-07-16T01:56:41Z)
• Learning Interpretable Models Through Multi-Objective Neural Architecture Search [0.9990687944474739]
  We propose a framework to optimize for both task performance and "introspectability," a surrogate metric for aspects of interpretability. We demonstrate that jointly optimizing for task error and introspectability leads to more disentangled and debuggable architectures that perform within error.
  arXiv  Detail & Related papers  (2021-12-16T05:50:55Z)
• 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)
• Rethinking Architecture Selection in Differentiable NAS [74.61723678821049]
  Differentiable Neural Architecture Search is one of the most popular NAS methods for its search efficiency and simplicity. We propose an alternative perturbation-based architecture selection that directly measures each operation's influence on the supernet. We find that several failure modes of DARTS can be greatly alleviated with the proposed selection method.
  arXiv  Detail & Related papers  (2021-08-10T00:53:39Z)
• iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients [75.41173109807735]
  Differentiable ARchiTecture Search (DARTS) has recently become the mainstream of neural architecture search (NAS) We tackle the hypergradient computation in DARTS based on the implicit function theorem. We show that the architecture optimisation with the proposed method, named iDARTS, is expected to converge to a stationary point.
  arXiv  Detail & Related papers  (2021-06-21T00:44:11Z)
• The Nonlinearity Coefficient -- A Practical Guide to Neural Architecture Design [3.04585143845864]
  We develop methods that can predict, without any training, whether an architecture will achieve a relatively high test or training error on a task after training. We then go on to explain the error in terms of the architecture definition itself and develop tools for modifying the architecture. Our first major contribution is to show that the 'degree of nonlinearity' of a neural architecture is a key causal driver behind its performance.
  arXiv  Detail & Related papers  (2021-05-25T20:47:43Z)
• Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? [22.63641173256389]
  Existing Neural Architecture Search (NAS) methods either encode neural architectures using discrete encodings that do not scale well, or adopt supervised learning-based methods to jointly learn architecture representations and optimize architecture search on such representations which incurs search bias. We observe that the structural properties of neural architectures are hard to preserve in the latent space if architecture representation learning and search are coupled, resulting in less effective search performance.
  arXiv  Detail & Related papers  (2020-06-12T04:15:34Z)
• 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)

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.