einspace: Searching for Neural Architectures from Fundamental Operations

• URL: http://arxiv.org/abs/2405.20838v2
• Date: Wed, 30 Oct 2024 12:35:56 GMT
• Title: einspace: Searching for Neural Architectures from Fundamental Operations
• Authors: Linus Ericsson, Miguel Espinosa, Chenhongyi Yang, Antreas Antoniou, Amos Storkey, Shay B. Cohen, Steven McDonagh, Elliot J. Crowley,
• Abstract summary: We introduce einspace, a search space based on a parameterised probabilistic context-free grammar. We show that competitive architectures can be obtained by searching from scratch, and we consistently find large improvements when initialising the search with strong baselines.
• Score: 28.346238250052455
• License:
• Abstract: Neural architecture search (NAS) finds high performing networks for a given task. Yet the results of NAS are fairly prosaic; they did not e.g. create a shift from convolutional structures to transformers. This is not least because the search spaces in NAS often aren't diverse enough to include such transformations a priori. Instead, for NAS to provide greater potential for fundamental design shifts, we need a novel expressive search space design which is built from more fundamental operations. To this end, we introduce einspace, a search space based on a parameterised probabilistic context-free grammar. Our space is versatile, supporting architectures of various sizes and complexities, while also containing diverse network operations which allow it to model convolutions, attention components and more. It contains many existing competitive architectures, and provides flexibility for discovering new ones. Using this search space, we perform experiments to find novel architectures as well as improvements on existing ones on the diverse Unseen NAS datasets. We show that competitive architectures can be obtained by searching from scratch, and we consistently find large improvements when initialising the search with strong baselines. We believe that this work is an important advancement towards a transformative NAS paradigm where search space expressivity and strategic search initialisation play key roles.

Related papers

• DNA Family: Boosting Weight-Sharing NAS with Block-Wise Supervisions [121.05720140641189]
  We develop a family of models with the distilling neural architecture (DNA) techniques. Our proposed DNA models can rate all architecture candidates, as opposed to previous works that can only access a sub- search space using algorithms. Our models achieve state-of-the-art top-1 accuracy of 78.9% and 83.6% on ImageNet for a mobile convolutional network and a small vision transformer, respectively.
  arXiv  Detail & Related papers  (2024-03-02T22:16:47Z)
• UnrealNAS: Can We Search Neural Architectures with Unreal Data? [84.78460976605425]
  Neural architecture search (NAS) has shown great success in the automatic design of deep neural networks (DNNs) Previous work has analyzed the necessity of having ground-truth labels in NAS and inspired broad interest. We take a further step to question whether real data is necessary for NAS to be effective.
  arXiv  Detail & Related papers  (2022-05-04T16:30:26Z)
• Towards Less Constrained Macro-Neural Architecture Search [2.685668802278155]
  Neural Architecture Search (NAS) networks achieve state-of-the-art performance in a variety of tasks. Most NAS methods rely heavily on human-defined assumptions that constrain the search. We present experiments showing that LCMNAS generates state-of-the-art architectures from scratch with minimal GPU computation.
  arXiv  Detail & Related papers  (2022-03-10T17:53:03Z)
• NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search [18.9676056830197]
  Most existing neural architecture search (NAS) benchmarks and algorithms prioritize performance on well-studied tasks. We present NAS-Bench-360, a benchmark suite for evaluating state-of-the-art NAS methods for convolutional neural networks (CNNs)
  arXiv  Detail & Related papers  (2021-10-12T01:13:18Z)
• TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search [98.22779489340869]
  We propose TransNAS-Bench-101, a benchmark dataset containing network performance across seven vision tasks. We explore two fundamentally different types of search space: cell-level search space and macro-level search space. With 7,352 backbones evaluated on seven tasks, 51,464 trained models with detailed training information are provided.
  arXiv  Detail & Related papers  (2021-05-25T12:15:21Z)
• Generative Adversarial Neural Architecture Search [21.05611902967155]
  We propose Generative Adversarial NAS (GA-NAS) with theoretically provable convergence guarantees. We show that GA-NAS can be used to improve already optimized baselines found by other NAS methods.
  arXiv  Detail & Related papers  (2021-05-19T18:54:44Z)
• GOLD-NAS: Gradual, One-Level, Differentiable [100.12492801459105]
  We propose a novel algorithm named Gradual One-Level Differentiable Neural Architecture Search (GOLD-NAS) It introduces a variable resource constraint to one-level optimization so that the weak operators are gradually pruned out from the super-network.
  arXiv  Detail & Related papers  (2020-07-07T10:37:49Z)
• Learning Architectures from an Extended Search Space for Language Modeling [37.79977691127229]
  We present a general approach to learn both intra-cell and inter-cell architectures of Neural architecture search (NAS) For recurrent neural language modeling, it outperforms a strong baseline significantly on the PTB and WikiText data, with a new state-of-the-art on PTB. The learned architectures show good transferability to other systems.
  arXiv  Detail & Related papers  (2020-05-06T05:02:33Z)
• NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search [55.12928953187342]
  We propose an extension to NAS-Bench-101: NAS-Bench-201 with a different search space, results on multiple datasets, and more diagnostic information. NAS-Bench-201 has a fixed search space and provides a unified benchmark for almost any up-to-date NAS algorithms. We provide additional diagnostic information such as fine-grained loss and accuracy, which can give inspirations to new designs of NAS algorithms.
  arXiv  Detail & Related papers  (2020-01-02T05:28:26Z)
• Scalable NAS with Factorizable Architectural Parameters [102.51428615447703]
  Neural Architecture Search (NAS) is an emerging topic in machine learning and computer vision. This paper presents a scalable algorithm by factorizing a large set of candidate operators into smaller subspaces. With a small increase in search costs and no extra costs in re-training, we find interesting architectures that were not explored before.
  arXiv  Detail & Related papers  (2019-12-31T10:26:56Z)

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.