Journal articles on the topic 'Broad Structural Representation Learning'
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Worden, Keith, and Graeme Manson. "The application of machine learning to structural health monitoring." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365, no. 1851 (December 12, 2006): 515–37. http://dx.doi.org/10.1098/rsta.2006.1938.
Full textArney, Noah D., and Hilary P. Krygsman. "Work-Integrated Learning Policy in Alberta: A Post-Structural Analysis." Canadian Journal of Educational Administration and Policy, no. 198 (February 17, 2022): 97–110. http://dx.doi.org/10.7202/1086429ar.
Full textQuintana, Rafael. "The ecology of human behavior: A network perspective." Methodological Innovations 15, no. 1 (March 2022): 42–61. http://dx.doi.org/10.1177/20597991221077911.
Full textBingman, Verner P., and Rubén N. Muzio. "Reflections on the Structural-Functional Evolution of the Hippocampus: What Is the Big Deal about a Dentate Gyrus." Brain, Behavior and Evolution 90, no. 1 (2017): 53–61. http://dx.doi.org/10.1159/000475592.
Full textSingh, Ajay, Harman Preet Singh, Fakhre Alam, and Vikas Agrawal. "Role of Education, Training, and E-Learning in Sustainable Employment Generation and Social Empowerment in Saudi Arabia." Sustainability 14, no. 14 (July 19, 2022): 8822. http://dx.doi.org/10.3390/su14148822.
Full textMcConaghy, Cathryn. "on Pedagogy, Trauma and Difficult Memory: Remembering Namatjira, our Beloved." Australian Journal of Indigenous Education 32 (2003): 11–20. http://dx.doi.org/10.1017/s1326011100003781.
Full textOrynbaikyzy, Aiym, Ursula Gessner, Benjamin Mack, and Christopher Conrad. "Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies." Remote Sensing 12, no. 17 (August 27, 2020): 2779. http://dx.doi.org/10.3390/rs12172779.
Full textFasoulis, Romanos, Georgios Paliouras, and Lydia E. Kavraki. "Graph representation learning for structural proteomics." Emerging Topics in Life Sciences 5, no. 6 (October 19, 2021): 789–802. http://dx.doi.org/10.1042/etls20210225.
Full textLi, Cheng-Te, and Hong-Yu Lin. "Structural Hierarchy-Enhanced Network Representation Learning." Applied Sciences 10, no. 20 (October 16, 2020): 7214. http://dx.doi.org/10.3390/app10207214.
Full textRomero, Lisa S. "Trust, behavior, and high school outcomes." Journal of Educational Administration 53, no. 2 (April 13, 2015): 215–36. http://dx.doi.org/10.1108/jea-07-2013-0079.
Full textNAMATAME, AKIRA, and YOSHIAKI TSUKAMOTO. "STRUCTURAL CONNECTIONIST LEARNING WITH COMPLEMENTARY CODING." International Journal of Neural Systems 03, no. 01 (January 1992): 19–30. http://dx.doi.org/10.1142/s0129065792000036.
Full textBrowne, Katie, and Monica Nicolescu. "Learning to Generalize from Demonstrations." Cybernetics and Information Technologies 12, no. 3 (September 1, 2012): 27–38. http://dx.doi.org/10.2478/cait-2012-0019.
Full textRouth, Prahlad K., Yang Liu, Nicholas Marcella, Boris Kozinsky, and Anatoly I. Frenkel. "Latent Representation Learning for Structural Characterization of Catalysts." Journal of Physical Chemistry Letters 12, no. 8 (February 23, 2021): 2086–94. http://dx.doi.org/10.1021/acs.jpclett.0c03792.
Full textFeng, Qiying, Zhulin Liu, and C. L. Philip Chen. "Broad and deep neural network for high-dimensional data representation learning." Information Sciences 599 (June 2022): 127–46. http://dx.doi.org/10.1016/j.ins.2022.03.058.
Full textLuo, Qi, Dongxiao Yu, Akshita Maradapu Vera Venkata Sai, Zhipeng Cai, and Xiuzhen Cheng. "A survey of structural representation learning for social networks." Neurocomputing 496 (July 2022): 56–71. http://dx.doi.org/10.1016/j.neucom.2022.04.128.
Full textMo, Yujie, Liang Peng, Jie Xu, Xiaoshuang Shi, and Xiaofeng Zhu. "Simple Unsupervised Graph Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7797–805. http://dx.doi.org/10.1609/aaai.v36i7.20748.
Full textJinpa, Tenzin, and Yong Gao. "Code Representation Learning Using Prüfer Sequences (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12977–78. http://dx.doi.org/10.1609/aaai.v36i11.21625.
Full textDu, Xin, Yulong Pei, Wouter Duivesteijn, and Mykola Pechenizkiy. "Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3809–16. http://dx.doi.org/10.1609/aaai.v34i04.5792.
Full textFROMMBERGER, LUTZ. "LEARNING TO BEHAVE IN SPACE: A QUALITATIVE SPATIAL REPRESENTATION FOR ROBOT NAVIGATION WITH REINFORCEMENT LEARNING." International Journal on Artificial Intelligence Tools 17, no. 03 (June 2008): 465–82. http://dx.doi.org/10.1142/s021821300800400x.
Full textJoaristi, Mikel, and Edoardo Serra. "SIR-GN: A Fast Structural Iterative Representation Learning Approach For Graph Nodes." ACM Transactions on Knowledge Discovery from Data 15, no. 6 (May 19, 2021): 1–39. http://dx.doi.org/10.1145/3450315.
Full textKuok, Sin-Chi, Ka-Veng Yuen, Mark Girolami, and Stephen Roberts. "Broad learning robust semi-active structural control: A nonparametric approach." Mechanical Systems and Signal Processing 162 (January 2022): 108012. http://dx.doi.org/10.1016/j.ymssp.2021.108012.
Full textVAN GOMPEL, ROGER P. G., and MANABU ARAI. "Structural priming in bilinguals." Bilingualism: Language and Cognition 21, no. 3 (October 5, 2017): 448–55. http://dx.doi.org/10.1017/s1366728917000542.
Full textKhajehnejad, Ahmad, Moein Khajehnejad, Mahmoudreza Babaei, Krishna P. Gummadi, Adrian Weller, and Baharan Mirzasoleiman. "CrossWalk: Fairness-Enhanced Node Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 11963–70. http://dx.doi.org/10.1609/aaai.v36i11.21454.
Full textWang, Yifei, Shiyang Chen, Guobin Chen, Ethan Shurberg, Hang Liu, and Pengyu Hong. "Motif-Based Graph Representation Learning with Application to Chemical Molecules." Informatics 10, no. 1 (January 11, 2023): 8. http://dx.doi.org/10.3390/informatics10010008.
Full textWang, Huayan, and Qiang Yang. "Transfer Learning by Structural Analogy." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 513–18. http://dx.doi.org/10.1609/aaai.v25i1.7907.
Full textFaber, Felix A., Anders S. Christensen, Bing Huang, and O. Anatole von Lilienfeld. "Alchemical and structural distribution based representation for universal quantum machine learning." Journal of Chemical Physics 148, no. 24 (June 28, 2018): 241717. http://dx.doi.org/10.1063/1.5020710.
Full textHe, Xiaoxu, Stephanie Leung, James Warrington, Olga Shmuilovich, and Shuo Li. "Automated neural foraminal stenosis grading via task-aware structural representation learning." Neurocomputing 287 (April 2018): 185–95. http://dx.doi.org/10.1016/j.neucom.2018.01.088.
Full textNguyen, Thanh Toan, Minh Tam Pham, Thanh Tam Nguyen, Thanh Trung Huynh, Van Vinh Tong, Quoc Viet Hung Nguyen, and Thanh Tho Quan. "Structural representation learning for network alignment with self-supervised anchor links." Expert Systems with Applications 165 (March 2021): 113857. http://dx.doi.org/10.1016/j.eswa.2020.113857.
Full textGarber, Dominik, and József Fiser. "The effect of emerging structural representation on spatial visual statistical learning." Journal of Vision 22, no. 14 (December 5, 2022): 3514. http://dx.doi.org/10.1167/jov.22.14.3514.
Full textLiu, Cuiwei, Zhaokui Li, Xiangbin Shi, and Chong Du. "Learning a Mid-Level Representation for Multiview Action Recognition." Advances in Multimedia 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/3508350.
Full textShi, Ying, Yan Zhao, and Nian Mao Deng. "Robust Object Tracking Based on Structural Local Sparse Representation and Incremental Subspace Learning." Advanced Materials Research 765-767 (September 2013): 2388–92. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2388.
Full textJafariakinabad, Fereshteh, and Kien A. Hua. "A Self-Supervised Representation Learning of Sentence Structure for Authorship Attribution." ACM Transactions on Knowledge Discovery from Data 16, no. 4 (August 31, 2022): 1–16. http://dx.doi.org/10.1145/3491203.
Full textIuchi, Hitoshi, Taro Matsutani, Keisuke Yamada, Natsuki Iwano, Shunsuke Sumi, Shion Hosoda, Shitao Zhao, Tsukasa Fukunaga, and Michiaki Hamada. "Representation learning applications in biological sequence analysis." Computational and Structural Biotechnology Journal 19 (2021): 3198–208. http://dx.doi.org/10.1016/j.csbj.2021.05.039.
Full textChen, C. L. Philip, Zhulin Liu, and Shuang Feng. "Universal Approximation Capability of Broad Learning System and Its Structural Variations." IEEE Transactions on Neural Networks and Learning Systems 30, no. 4 (April 2019): 1191–204. http://dx.doi.org/10.1109/tnnls.2018.2866622.
Full textYe, Zhonglin, Haixing Zhao, Ke Zhang, Yu Zhu, and Zhaoyang Wang. "An Optimized Network Representation Learning Algorithm Using Multi-Relational Data." Mathematics 7, no. 5 (May 21, 2019): 460. http://dx.doi.org/10.3390/math7050460.
Full textNido, Gonzalo S., Ludovica Bachschmid-Romano, Ugo Bastolla, and Alberto Pascual-García. "Learning structural bioinformatics and evolution with a snake puzzle." PeerJ Computer Science 2 (December 5, 2016): e100. http://dx.doi.org/10.7717/peerj-cs.100.
Full textSun, Wei-Xiang, and Hui Xue. "Learning graph-level representation from local-structural distribution with Graph Neural Networks." Knowledge-Based Systems 230 (October 2021): 107383. http://dx.doi.org/10.1016/j.knosys.2021.107383.
Full textLi, Ao, Xin Liu, Yanbing Wang, Deyun Chen, Kezheng Lin, Guanglu Sun, and Hailong Jiang. "Subspace structural constraint-based discriminative feature learning via nonnegative low rank representation." PLOS ONE 14, no. 5 (May 7, 2019): e0215450. http://dx.doi.org/10.1371/journal.pone.0215450.
Full textWang, Jing, Shubin Lyu, Junwei Duan, and Zhengchun Lin. "Sparse Enhancement Fuzzy Broad Learning System Based on Multiple Clustering Methods." Journal of Physics: Conference Series 2203, no. 1 (February 1, 2022): 012068. http://dx.doi.org/10.1088/1742-6596/2203/1/012068.
Full textXiao, Yabo, Dongdong Yu, Xiao Juan Wang, Lei Jin, Guoli Wang, and Qian Zhang. "Learning Quality-Aware Representation for Multi-Person Pose Regression." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 2822–30. http://dx.doi.org/10.1609/aaai.v36i3.20186.
Full textZhou, Xiaojie, Pengjun Zhai, and Yu Fang. "Learning Description-Based Representations for Temporal Knowledge Graph Reasoning via Attentive CNN." Journal of Physics: Conference Series 2025, no. 1 (September 1, 2021): 012003. http://dx.doi.org/10.1088/1742-6596/2025/1/012003.
Full textMinematsu, Nobuaki. "Structural representation of the pronunciation and its application to computer‐aided language learning." Journal of the Acoustical Society of America 120, no. 5 (November 2006): 3137–38. http://dx.doi.org/10.1121/1.4787745.
Full textZhang, Weihang, Ovidiu Șerban, Jiahao Sun, and Yike Guo. "IPPT4KRL: Iterative Post-Processing Transfer for Knowledge Representation Learning." Machine Learning and Knowledge Extraction 5, no. 1 (January 6, 2023): 43–58. http://dx.doi.org/10.3390/make5010004.
Full textZhang, Liyan, Jingfeng Guo, Jiazheng Wang, Jing Wang, Shanshan Li, and Chunying Zhang. "Hypergraph and Uncertain Hypergraph Representation Learning Theory and Methods." Mathematics 10, no. 11 (June 3, 2022): 1921. http://dx.doi.org/10.3390/math10111921.
Full textMu, Shanlei, Yaliang Li, Wayne Xin Zhao, Siqing Li, and Ji-Rong Wen. "Knowledge-Guided Disentangled Representation Learning for Recommender Systems." ACM Transactions on Information Systems 40, no. 1 (January 31, 2022): 1–26. http://dx.doi.org/10.1145/3464304.
Full textBin, Chenzhong, Saige Qin, Guanjun Rao, Tianlong Gu, and Liang Chang. "Multiview Translation Learning for Knowledge Graph Embedding." Scientific Programming 2020 (August 25, 2020): 1–9. http://dx.doi.org/10.1155/2020/7084958.
Full textRosafalco, Luca, Andrea Manzoni, Stefano Mariani, and Alberto Corigliano. "An Autoencoder-Based Deep Learning Approach for Load Identification in Structural Dynamics." Sensors 21, no. 12 (June 19, 2021): 4207. http://dx.doi.org/10.3390/s21124207.
Full textUsher, Bethany, and Stephanie Hazel. "Students as scholars courses and student learning outcomes: A realignment." Innovations in Teaching & Learning Conference Proceedings 8 (July 15, 2016): 2. http://dx.doi.org/10.13021/g8gs31.
Full textJarosz, Gaja. "Computational Modeling of Phonological Learning." Annual Review of Linguistics 5, no. 1 (January 14, 2019): 67–90. http://dx.doi.org/10.1146/annurev-linguistics-011718-011832.
Full textKuok, Sin-Chi, and Ka-Veng Yuen. "Model-free data reconstruction of structural response and excitation via sequential broad learning." Mechanical Systems and Signal Processing 141 (July 2020): 106738. http://dx.doi.org/10.1016/j.ymssp.2020.106738.
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