evolving attention with residual convolutions

Keyword	CPC	PCC	Volume	Score	Length of keyword
evolving attention with residual convolutions	1.04	0.2	9010	1	45
evolving	0.65	0.7	1899	92	8
attention	0.99	0.6	3969	54	9
with	1.07	0.6	4435	44	4
residual	0.08	1	7675	25	8
convolutions	1.24	0.1	3947	44	12

Keyword	CPC	PCC	Volume	Score
evolving attention with residual convolutions	0.95	0.9	3163	36
residual-convolution	1.79	0.7	9272	36
residual convolution neural network	0.05	0.5	9576	16
residual convolutional neural network	0.46	1	3732	87
dilated convolutional residual network	1.15	0.7	4679	11
convolutional residual memory networks	1.77	0.3	6568	96
convolutional self-attention	0.41	0.8	42	81
residual graph convolutional network	0.48	0.4	946	31
residual_attention	0.04	0.4	375	65
fully convolutional residual networks	1.32	0.5	8273	4
re-considering convolution	0.55	0.3	9351	10
attention-augmented convolution	1.79	1	3288	91
convolutional_block_attention	0.95	0.3	4115	20
convolution+relu	0.95	0.2	817	58
attention in convolutional neural networks	1.05	0.9	6880	80
deep residual convolutional neural network	1.12	0.6	1737	76
how to understand convolution	0.8	1	7131	92
attention augmented convolutional networks	0.5	0.1	6646	44
detail-enhanced convolution	1.31	0.5	4945	93
attention over convolution kernels	0.03	0.7	2539	63
convolutional_en	1.69	0.1	9067	13

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[2102.12895] Evolving Attention with Residual Convolutions - arXiv.org
arxiv.org

https://arxiv.org/abs/2102.12895

Authors: Wang, Yujing, Yang, Yaming, Bai, Jiangang, Zhang, Minglian… Published: 2021/02/20 RelatedEntitiesURL: http://arxiv.org/pdf/2102.12895.pdf Subject: Machine Learning (cs.LG) Cite As: arXiv:2102.12895 [cs.LG]

Authors: Wang, Yujing, Yang, Yaming, Bai, Jiangang, Zhang, Minglian…
Published: 2021/02/20
RelatedEntitiesURL: http://arxiv.org/pdf/2102.12895.pdf
Subject: Machine Learning (cs.LG)
Cite As: arXiv:2102.12895 [cs.LG]

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https://ar5iv.labs.arxiv.org/html/2102.12895

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Evolving Attention with Residual Convolutions - arXiv.org
arxiv.org

https://arxiv.org/pdf/2102.12895.pdf

Abstract3.1. Overview3.4. Value Projection and Feed-Forward Layers6. ConclusionC. Case StudyIn this paper, we propose a novel mechanism, Evolving Attention for Transformers, which facilitates the learning of attention maps via a chain of residual convolutional neural networks. It obtains superior performance in various tasks in both CV and NLP domains. Future works are considered in three aspects. First, we will apply evolving attention t...See more on arxiv.orgExplore further In this paper, we propose a novel mechanism, Evolving Attention for Transformers, which facilitates the learning of attention maps via a chain of residual convolutional neural networks. It obtains superior performance in various tasks in both CV and NLP domains. Future works are considered in three aspects. First, we will apply evolving attention t...

In this paper, we propose a novel mechanism, Evolving Attention for Transformers, which facilitates the learning of attention maps via a chain of residual convolutional neural networks. It obtains superior performance in various tasks in both CV and NLP domains. Future works are considered in three aspects. First, we will apply evolving attention t...

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Evolving Attention with Residual Convolutions - arXiv.org
arxiv.org

https://arxiv.org/pdf/2102.12895.pdf

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Evolving Attention with Residual Convolutions - Proceedings of …
mlr.press

http://proceedings.mlr.press/v139/wang21ab/wang21ab.pdf

WEBA novel mechanism to improve transformer models by sharing and evolving attention maps across layers with convolutional modules. The paper presents the design, analysis … File Size: 1MB Author: Yujing Wang, Yaming Yang, Jiangang Bai, Mingliang Zhang, Jing Bai, Jing Yu, Ce Zhang, Gao Huang, Yun... Page Count: 10 Publish Year: 2021

File Size: 1MB
Author: Yujing Wang, Yaming Yang, Jiangang Bai, Mingliang Zhang, Jing Bai, Jing Yu, Ce Zhang, Gao Huang, Yun...
Page Count: 10
Publish Year: 2021

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Papers with Code - Evolving Attention with Residual Convolutions
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https://paperswithcode.com/paper/evolving-attention-with-residual-convolutions

WEBFeb 20, 2021 · A novel and generic mechanism to improve transformers by sharing and evolving attention maps across layers. The paper presents results for image …

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Evolving Attention with Residual Convolutions - PMLR
mlr.press

https://proceedings.mlr.press/v139/wang21ab.html

WEBJul 1, 2021 · Evolving Attention with Residual Convolutions. Yujing Wang, Yaming Yang, Jiangang Bai, Mingliang Zhang, Jing Bai, Jing Yu, Ce Zhang, Gao Huang, Yunhai Tong. … Author: Yujing Wang, Yaming Yang, Jiangang Bai, Mingliang Zhang, Jing Bai, Jing Yu, Ce Zhang, Gao Huang, Yun... Publish Year: 2021

Author: Yujing Wang, Yaming Yang, Jiangang Bai, Mingliang Zhang, Jing Bai, Jing Yu, Ce Zhang, Gao Huang, Yun...
Publish Year: 2021

DA: 31 PA: 31 MOZ Rank: 28

ICML 2021 Evolving Attention with Residual Convolutions Spotlight
icml.cc

https://icml.cc/virtual/2021/spotlight/8882

WEBA novel and generic mechanism to improve transformers for natural language processing and computer vision. The attention maps in different layers share common knowledge and …

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Evolving Attention with Residual Convolutions - Microsoft Research
microsoft.com

https://www.microsoft.com/en-us/research/publication/evolving-attention-with-residual-convolutions/

WEBIn this paper, we propose a novel and generic mechanism based on evolving attention to improve the performance of transformers. On one hand, the attention maps in different …

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[2102.12895] Evolving Attention with Residual Convolutions - arXiv …
arxiv.org

https://ar5iv.labs.arxiv.org/html/2102.12895

WEBIn this paper, we propose a novel mechanism, Evolving Attention for Transformers, which facilitates the learning of attention maps via a chain of residual convolutional neural …

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Evolving Attention with Residual Convolutions - NASA/ADS
harvard.edu

https://ui.adsabs.harvard.edu/abs/2021arXiv210212895W/abstract

WEBHuang, Gao. ; Tong, Yunhai. Transformer is a ubiquitous model for natural language processing and has attracted wide attentions in computer vision. The attention maps are …

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