Results 21 to 30 of about 5,906,664 (269)
Exploring Self-Attention for Image Recognition [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 CVPR 2020, Code available at https://github.com/hszhao ...
Hengshuang Zhao +2 more
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Denoising Self-Attentive Sequential Recommendation
Proceedings of the 16th ACM Conference on Recommender Systems, 2022 Transformer-based sequential recommenders are very powerful for capturing both short-term and long-term sequential item dependencies. This is mainly attributed to their unique self-attention networks to exploit pairwise item-item interactions within the sequence.
Huiyuan Chen +8 more
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CoRR, 2019 Advanced methods of applying deep learning to structured data such as graphs have been proposed in recent years. In particular, studies have focused on generalizing convolutional neural networks to graph data, which includes redefining the convolution and the downsampling (pooling) operations for graphs.
Junhyun Lee, Inyeop Lee, Jaewoo Kang
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Introduce the Result Into Self-Attention
CoRR, 2021 Traditional self-attention mechanisms in convolutional networks tend to use only the output of the previous layer as input to the attention network, such as SENet, CBAM, etc. In this paper, we propose a new attention modification method that tries to get the output of the classification network in advance and use it as a part of the input of the ...
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The function of the self-attention network [PDF]
Cognitive Neuroscience, 2015 This commentary links Humphrey and Sui's proposed Self-attention Network (SAN) to the memory advantage associated with self-relevant information (i.e., the self-reference effect). Articulating this link elucidates the functional quality of the SAN in ensuring that information of potential importance to self is not lost.
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Self-attentive Adversarial Stain Normalization
, 2021 Hematoxylin and Eosin (H&E) stained Whole Slide Images (WSIs) are utilized for biopsy visualization-based diagnostic and prognostic assessment of diseases. Variation in the H&E staining process across different lab sites can lead to significant variations in biopsy image appearance. These variations introduce an undesirable bias when the slides
Aman Shrivastava +9 more
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Self-Attentive Hawkes Processes
CoRR, 2019 Asynchronous events on the continuous time domain, e.g., social media actions and stock transactions, occur frequently in the world. The ability to recognize occurrence patterns of event sequences is crucial to predict which typeof events will happen next and when. A de facto standard mathematical framework to do this is the Hawkes process. In order to
Qiang Zhang 0026 +3 more
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Self-attentive Model for Headline Generation [PDF]
, 2019 Headline generation is a special type of text summarization task. While the amount of available training data for this task is almost unlimited, it still remains challenging, as learning to generate headlines for news articles implies that the model has strong reasoning about natural language.
Daniil Gavrilov +2 more
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Spatial self-attention network with self-attention distillation for fine-grained image recognition
Journal of Visual Communication and Image Representation, 2021 Abstract The underlining task for fine-grained image recognition captures both the inter-class and intra-class discriminate features. Existing methods generally use auxiliary data to guide the network or a complex network comprising multiple sub-networks.
Adu Asare Baffour +4 more
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