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Relevant bibliographies by topics / Hierarchical Pooling

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Academic literature on the topic 'Hierarchical Pooling'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Hierarchical Pooling"

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Fernando, Basura, and Stephen Gould. "Discriminatively Learned Hierarchical Rank Pooling Networks." International Journal of Computer Vision 124, no. 3 (2017): 335–55. http://dx.doi.org/10.1007/s11263-017-1030-x.

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Ranjan, Ekagra, Soumya Sanyal, and Partha Talukdar. "ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5470–77. http://dx.doi.org/10.1609/aaai.v34i04.5997.

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Graph Neural Networks (GNN) have been shown to work effectively for modeling graph structured data to solve tasks such as node classification, link prediction and graph classification. There has been some recent progress in defining the notion of pooling in graphs whereby the model tries to generate a graph level representation by downsampling and summarizing the information present in the nodes. Existing pooling methods either fail to effectively capture the graph substructure or do not easily scale to large graphs. In this work, we propose ASAP (Adaptive Structure Aware Pooling), a sparse an

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Chen, Jiawang, and Zhenqiang Wu. "Learning Embedding for Signed Network in Social Media with Hierarchical Graph Pooling." Applied Sciences 12, no. 19 (2022): 9795. http://dx.doi.org/10.3390/app12199795.

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Signed network embedding concentrates on learning fixed-length representations for nodes in signed networks with positive and negative links, which contributes to many downstream tasks in social media, such as link prediction. However, most signed network embedding approaches neglect hierarchical graph pooling in the networks, limiting the capacity to learn genuine signed graph topology. To overcome this limitation, this paper presents a unique deep learning-based Signed network embedding model with Hierarchical Graph Pooling (SHGP). To be more explicit, a hierarchical pooling mechanism has be

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Grumitt, R. D. P., Luke R. P. Jew, and C. Dickinson. "Hierarchical Bayesian CMB component separation with the No-U-Turn Sampler." Monthly Notices of the Royal Astronomical Society 496, no. 4 (2020): 4383–401. http://dx.doi.org/10.1093/mnras/staa1857.

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ABSTRACT In this paper, we present a novel implementation of Bayesian cosmic microwave background (CMB) component separation. We sample from the full posterior distribution using the No-U-Turn Sampler (NUTS), a gradient-based sampling algorithm. Alongside this, we introduce new foreground modelling approaches. We use the mean shift algorithm to define regions on the sky, clustering according to naively estimated foreground spectral parameters. Over these regions we adopt a complete pooling model, where we assume constant spectral parameters, and a hierarchical model, where we model individual

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Devineni, Naresh, Upmanu Lall, Neil Pederson, and Edward Cook. "A Tree-Ring-Based Reconstruction of Delaware River Basin Streamflow Using Hierarchical Bayesian Regression." Journal of Climate 26, no. 12 (2013): 4357–74. http://dx.doi.org/10.1175/jcli-d-11-00675.1.

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Abstract A hierarchical Bayesian regression model is presented for reconstructing the average summer streamflow at five gauges in the Delaware River basin using eight regional tree-ring chronologies. The model provides estimates of the posterior probability distribution of each reconstructed streamflow series considering parameter uncertainty. The vectors of regression coefficients are modeled as draws from a common multivariate normal distribution with unknown parameters estimated as part of the analysis. This leads to a multilevel structure. The covariance structure of the streamflow residua

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Chen, Junying, and Ying Chen. "Saliency Enhanced Hierarchical Bilinear Pooling for Fine-Grained Classification." Journal of Computer-Aided Design & Computer Graphics 33, no. 2 (2021): 241–49. http://dx.doi.org/10.3724/sp.j.1089.2021.18399.

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Sanchez-Giraldo, Luis G., Md Nasir Uddin Laskar, and Odelia Schwartz. "Normalization and pooling in hierarchical models of natural images." Current Opinion in Neurobiology 55 (April 2019): 65–72. http://dx.doi.org/10.1016/j.conb.2019.01.008.

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Tan, Min, Fu Yuan, Jun Yu, Guijun Wang, and Xiaoling Gu. "Fine-grained Image Classification via Multi-scale Selective Hierarchical Biquadratic Pooling." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 1s (2022): 1–23. http://dx.doi.org/10.1145/3492221.

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How to extract distinctive features greatly challenges the fine-grained image classification tasks. In previous models, bilinear pooling has been frequently adopted to address this problem. However, most bilinear pooling models neglect either intra or inter layer feature interaction. This insufficient interaction brings in the loss of discriminative information. In this article, we devise a novel fine-grained image classification approach named M ulti-scale S elective H ierarchical bi Q uadratic P ooling (MSHQP). The proposed biquadratic pooling simultaneously models intra and inter layer feat

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Ko, Sung Moon, Sungjun Cho, Dae-Woong Jeong, Sehui Han, Moontae Lee, and Honglak Lee. "Grouping Matrix Based Graph Pooling with Adaptive Number of Clusters." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8334–42. http://dx.doi.org/10.1609/aaai.v37i7.26005.

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Graph pooling is a crucial operation for encoding hierarchical structures within graphs. Most existing graph pooling approaches formulate the problem as a node clustering task which effectively captures the graph topology. Conventional methods ask users to specify an appropriate number of clusters as a hyperparameter, then assuming that all input graphs share the same number of clusters. In inductive settings where the number of clusters could vary, however, the model should be able to represent this variation in its pooling layers in order to learn suitable clusters. Thus we propose GMPool, a

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Li, Keqin. "Hierarchical Pooling Strategy Optimization for Accelerating Asymptomatic COVID-19 Screening." IEEE Open Journal of the Computer Society 1 (2020): 276–84. http://dx.doi.org/10.1109/ojcs.2020.3036581.

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Dissertations / Theses on the topic "Hierarchical Pooling"

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Mazari, Ahmed. "Apprentissage profond pour la reconnaissance d’actions en vidéos." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS171.

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De nos jours, les contenus vidéos sont omniprésents grâce à Internet et les smartphones, ainsi que les médias sociaux. De nombreuses applications de la vie quotidienne, telles que la vidéo surveillance et la description de contenus vidéos, ainsi que la compréhension de scènes visuelles, nécessitent des technologies sophistiquées pour traiter les données vidéos. Il devient nécessaire de développer des moyens automatiques pour analyser et interpréter la grande quantité de données vidéo disponibles. Dans cette thèse, nous nous intéressons à la reconnaissance d'actions dans les vidéos, c.a.d au pr

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Book chapters on the topic "Hierarchical Pooling"

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Zhang, Can, Yuexian Zou, and Guang Chen. "Hierarchical Temporal Pooling for Efficient Online Action Recognition." In MultiMedia Modeling. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05710-7_39.

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Yu, Chaojian, Xinyi Zhao, Qi Zheng, Peng Zhang, and Xinge You. "Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition." In Computer Vision – ECCV 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01270-0_35.

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Liu, Yan, Zhi Liu, and Zhirong Lei. "Hierarchical Pooling Based Extreme Learning Machine for Image Classification." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9698-5_1.

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Thornton, John, Jolon Faichney, Michael Blumenstein, and Trevor Hine. "Character Recognition Using Hierarchical Vector Quantization and Temporal Pooling." In AI 2008: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89378-3_57.

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Fei, Xiaohan, Konstantine Tsotsos, and Stefano Soatto. "A Simple Hierarchical Pooling Data Structure for Loop Closure." In Computer Vision – ECCV 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46487-9_20.

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Liu, Peishuo, Cangqi Zhou, Xiao Liu, Jing Zhang, and Qianmu Li. "Multi-Granularity Contrastive Learning for Graph with Hierarchical Pooling." In Artificial Neural Networks and Machine Learning – ICANN 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44216-2_41.

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Zhao, Haifeng, Xiaoping Wu, Dejun Bao, and Shaojie Zhang. "Intracranial Hematoma Classification Based on the Pyramid Hierarchical Bilinear Pooling." In Pattern Recognition and Computer Vision. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88010-1_51.

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Liu, Wenya, Zhi Yang, Haitao Gan, Zhongwei Huang, Ran Zhou, and Ming Shi. "Hierarchical Pooling Graph Convolutional Neural Network for Alzheimer’s Disease Diagnosis." In PRICAI 2023: Trends in Artificial Intelligence. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7019-3_39.

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Bandyopadhyay, Sambaran, Manasvi Aggarwal, and M. Narasimha Murty. "A Deep Hybrid Pooling Architecture for Graph Classification with Hierarchical Attention." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75762-5_44.

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Otter, Thomas, and Tetyana Kosyakova. "Implications of Linear Versus Dummy Coding for Pooling of Information in Hierarchical Models." In Quantitative Marketing and Marketing Management. Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-3722-3_8.

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Conference papers on the topic "Hierarchical Pooling"

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Pan, Zizheng, Bohan Zhuang, Jing Liu, Haoyu He, and Jianfei Cai. "Scalable Vision Transformers with Hierarchical Pooling." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00043.

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Fernando, Basura, Peter Anderson, Marcus Hutter, and Stephen Gould. "Discriminative Hierarchical Rank Pooling for Activity Recognition." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.212.

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Bi, Liande, Xin Sun, Fei Zhou, and Junyu Dong. "Hierarchical Triplet Attention Pooling for Graph Classification." In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2021. http://dx.doi.org/10.1109/ictai52525.2021.00100.

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Ali, Waqar, Sebastiano Vascon, Thilo Stadelmann, and Marcello Pelillo. "Quasi-CliquePool: Hierarchical Graph Pooling for Graph Classification." In SAC '23: 38th ACM/SIGAPP Symposium on Applied Computing. ACM, 2023. http://dx.doi.org/10.1145/3555776.3578600.

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Roy, Kashob Kumar, Amit Roy, A. K. M. Mahbubur Rahman, M. Ashraful Amin, and Amin Ahsan Ali. "Structure-Aware Hierarchical Graph Pooling using Information Bottleneck." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533778.

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Su, Zidong, Zehui Hu, and Yangding Li. "Hierarchical Graph Representation Learning with Local Capsule Pooling." In MMAsia '21: ACM Multimedia Asia. ACM, 2021. http://dx.doi.org/10.1145/3469877.3495645.

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He, Ke-Xin, Yu-Han Shen, and Wei-Qiang Zhang. "Hierarchical Pooling Structure for Weakly Labeled Sound Event Detection." In Interspeech 2019. ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-2049.

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Rachmadi, Reza Fuad, Keiichi Uchimura, Gou Koutaki, and Kohichi Ogata. "Hierarchical Spatial Pyramid Pooling for Fine-Grained Vehicle Classification." In 2018 International Workshop on Big Data and Information Security (IWBIS). IEEE, 2018. http://dx.doi.org/10.1109/iwbis.2018.8471695.

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Gao, Lijian, Ling Zhou, Qirong Mao, and Ming Dong. "Adaptive Hierarchical Pooling for Weakly-supervised Sound Event Detection." In MM '22: The 30th ACM International Conference on Multimedia. ACM, 2022. http://dx.doi.org/10.1145/3503161.3548097.

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Yu, Hualei, Yirong Yao, Jinliang Yuan, and Chongjun Wang. "DIPool: Degree-Induced Pooling for Hierarchical Graph Representation Learning." In 2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2022. http://dx.doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom57177.2022.00035.

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