@inproceedings{xi-etal-2023-connectivity,
title = "Connectivity Patterns are Task Embeddings",
author = "Xi, Zhiheng and
Zheng, Rui and
Zhang, Yuansen and
Huang, Xuanjing and
Wei, Zhongyu and
Peng, Minlong and
Sun, Mingming and
Zhang, Qi and
Gui, Tao",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.759",
doi = "10.18653/v1/2023.findings-acl.759",
pages = "11993--12013",
abstract = "Task embeddings are task-specific vectors designed to construct a semantic space of tasks, which can be used to predict the most transferable source task for a given target task via the similarity between task embeddings. However, existing methods use optimized parameters and representations as task embeddings, resulting in substantial computational complexity and storage requirements. In this work, we draw inspiration from the operating mechanism of deep neural networks (DNNs) and biological brains, where neuronal activations are sparse and task-specific, and we use the connectivity patterns of neurons as a unique identifier associated with the task. The proposed method learns to assign importance masks for sub-structures of DNNs, and accordingly indicate the task-specific connectivity patterns. In addition to the storage advantages brought by the binary masking mechanism and structured sparsity, the early-bird nature of the sparse optimization process can deliver an efficient computation advantage. Experiments show that our method consistently outperforms other baselines in predicting inter-task transferability across data regimes and transfer settings, while keeping high efficiency in computation and storage.",
}
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<abstract>Task embeddings are task-specific vectors designed to construct a semantic space of tasks, which can be used to predict the most transferable source task for a given target task via the similarity between task embeddings. However, existing methods use optimized parameters and representations as task embeddings, resulting in substantial computational complexity and storage requirements. In this work, we draw inspiration from the operating mechanism of deep neural networks (DNNs) and biological brains, where neuronal activations are sparse and task-specific, and we use the connectivity patterns of neurons as a unique identifier associated with the task. The proposed method learns to assign importance masks for sub-structures of DNNs, and accordingly indicate the task-specific connectivity patterns. In addition to the storage advantages brought by the binary masking mechanism and structured sparsity, the early-bird nature of the sparse optimization process can deliver an efficient computation advantage. Experiments show that our method consistently outperforms other baselines in predicting inter-task transferability across data regimes and transfer settings, while keeping high efficiency in computation and storage.</abstract>
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%0 Conference Proceedings
%T Connectivity Patterns are Task Embeddings
%A Xi, Zhiheng
%A Zheng, Rui
%A Zhang, Yuansen
%A Huang, Xuanjing
%A Wei, Zhongyu
%A Peng, Minlong
%A Sun, Mingming
%A Zhang, Qi
%A Gui, Tao
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F xi-etal-2023-connectivity
%X Task embeddings are task-specific vectors designed to construct a semantic space of tasks, which can be used to predict the most transferable source task for a given target task via the similarity between task embeddings. However, existing methods use optimized parameters and representations as task embeddings, resulting in substantial computational complexity and storage requirements. In this work, we draw inspiration from the operating mechanism of deep neural networks (DNNs) and biological brains, where neuronal activations are sparse and task-specific, and we use the connectivity patterns of neurons as a unique identifier associated with the task. The proposed method learns to assign importance masks for sub-structures of DNNs, and accordingly indicate the task-specific connectivity patterns. In addition to the storage advantages brought by the binary masking mechanism and structured sparsity, the early-bird nature of the sparse optimization process can deliver an efficient computation advantage. Experiments show that our method consistently outperforms other baselines in predicting inter-task transferability across data regimes and transfer settings, while keeping high efficiency in computation and storage.
%R 10.18653/v1/2023.findings-acl.759
%U https://aclanthology.org/2023.findings-acl.759
%U https://doi.org/10.18653/v1/2023.findings-acl.759
%P 11993-12013
Markdown (Informal)
[Connectivity Patterns are Task Embeddings](https://aclanthology.org/2023.findings-acl.759) (Xi et al., Findings 2023)
ACL
- Zhiheng Xi, Rui Zheng, Yuansen Zhang, Xuanjing Huang, Zhongyu Wei, Minlong Peng, Mingming Sun, Qi Zhang, and Tao Gui. 2023. Connectivity Patterns are Task Embeddings. In Findings of the Association for Computational Linguistics: ACL 2023, pages 11993–12013, Toronto, Canada. Association for Computational Linguistics.