Artigos de revistas sobre o tema "Shortcut learning"
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Kim, Doyoung, Dongmin Park, Yooju Shin, Jihwan Bang, Hwanjun Song e Jae-Gil Lee. "Adaptive Shortcut Debiasing for Online Continual Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 12 (24 de março de 2024): 13122–31. http://dx.doi.org/10.1609/aaai.v38i12.29211.
Texto completo da fonteNauta, Meike, Ricky Walsh, Adam Dubowski e Christin Seifert. "Uncovering and Correcting Shortcut Learning in Machine Learning Models for Skin Cancer Diagnosis". Diagnostics 12, n.º 1 (24 de dezembro de 2021): 40. http://dx.doi.org/10.3390/diagnostics12010040.
Texto completo da fonteGeirhos, Robert, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge e Felix A. Wichmann. "Shortcut learning in deep neural networks". Nature Machine Intelligence 2, n.º 11 (novembro de 2020): 665–73. http://dx.doi.org/10.1038/s42256-020-00257-z.
Texto completo da fonteFay, Louisa, Erick Cobos, Bin Yang, Sergios Gatidis e Thomas Küstner. "Avoiding Shortcut-Learning by Mutual Information Minimization in Deep Learning-Based Image Processing". IEEE Access 11 (2023): 64070–86. http://dx.doi.org/10.1109/access.2023.3289397.
Texto completo da fontePOTAPOV, ALEXEI B., e M. K. ALI. "LEARNING, EXPLORATION AND CHAOTIC POLICIES". International Journal of Modern Physics C 11, n.º 07 (outubro de 2000): 1455–64. http://dx.doi.org/10.1142/s0129183100001309.
Texto completo da fonteMORIHIRO, KOICHIRO, NOBUYUKI MATSUI e HARUHIKO NISHIMURA. "CHAOTIC EXPLORATION EFFECTS ON REINFORCEMENT LEARNING IN SHORTCUT MAZE TASK". International Journal of Bifurcation and Chaos 16, n.º 10 (outubro de 2006): 3015–22. http://dx.doi.org/10.1142/s0218127406016616.
Texto completo da fonteDu, Mengnan, Fengxiang He, Na Zou, Dacheng Tao e Xia Hu. "Shortcut Learning of Large Language Models in Natural Language Understanding". Communications of the ACM 67, n.º 1 (21 de dezembro de 2023): 110–20. http://dx.doi.org/10.1145/3596490.
Texto completo da fonteHAN, FANG, MARIAN WIERCIGROCH, JIAN-AN FANG e ZHIJIE WANG. "EXCITEMENT AND SYNCHRONIZATION OF SMALL-WORLD NEURONAL NETWORKS WITH SHORT-TERM SYNAPTIC PLASTICITY". International Journal of Neural Systems 21, n.º 05 (outubro de 2011): 415–25. http://dx.doi.org/10.1142/s0129065711002924.
Texto completo da fonteHu, Ruilin, Yajun Du, Jingrong Hu e Hui Li. "Cross-community shortcut detection based on network representation learning and structural features". Intelligent Data Analysis 27, n.º 3 (18 de maio de 2023): 709–32. http://dx.doi.org/10.3233/ida-216513.
Texto completo da fonteZhong, Yujie, Xiao Li, Jiangjian Xie e Junguo Zhang. "A Lightweight Automatic Wildlife Recognition Model Design Method Mitigating Shortcut Learning". Animals 13, n.º 5 (25 de fevereiro de 2023): 838. http://dx.doi.org/10.3390/ani13050838.
Texto completo da fonteTrivedi, Anusua, Caleb Robinson, Marian Blazes, Anthony Ortiz, Jocelyn Desbiens, Sunil Gupta, Rahul Dodhia et al. "Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement". PLOS ONE 17, n.º 10 (6 de outubro de 2022): e0274098. http://dx.doi.org/10.1371/journal.pone.0274098.
Texto completo da fonteLao, Mingrui, Nan Pu, Yu Liu, Kai He, Erwin M. Bakker e Michael S. Lew. "COCA: COllaborative CAusal Regularization for Audio-Visual Question Answering". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 11 (26 de junho de 2023): 12995–3003. http://dx.doi.org/10.1609/aaai.v37i11.26527.
Texto completo da fonteNatsir, Siti Zahra Mulianti, Bibin Rubini, Didit Ardianto e Nurhaedah Madjid. "Interactive Learning Multimedia: A Shortcut for Boosting Gen-Z’s Digital literacy in Science Classroom". Jurnal Penelitian Pendidikan IPA 8, n.º 5 (30 de novembro de 2022): 2168–75. http://dx.doi.org/10.29303/jppipa.v8i5.1897.
Texto completo da fonteFathima, Sheeba. "Music Genre Classification using Deep Learning". International Journal for Research in Applied Science and Engineering Technology 9, n.º VII (10 de julho de 2021): 66–71. http://dx.doi.org/10.22214/ijraset.2021.36087.
Texto completo da fonteRees, Simon, Megan Bruce e Steven Bradley. "Utilising data-driven learning in chemistry teaching: A shortcut to improving chemical language comprehension". New Directions in the Teaching of Physical Sciences, n.º 10 (1 de junho de 2014): 12–19. http://dx.doi.org/10.29311/ndtps.v0i10.511.
Texto completo da fonteRees, Simon, Megan Bruce e Steven Bradley. "Utilising Data-driven Learning in Chemistry Teaching: a Shortcut to Improving Chemical Language Comprehension". New Directions 10, n.º 1 (junho de 2014): 12–19. http://dx.doi.org/10.11120/ndir.2014.00028.
Texto completo da fonteWilkinson, Anna, Karin Kuenstner, Julia Mueller e Ludwig Huber. "Social learning in a non-social reptile ( Geochelone carbonaria )". Biology Letters 6, n.º 5 (31 de março de 2010): 614–16. http://dx.doi.org/10.1098/rsbl.2010.0092.
Texto completo da fonteMengue-Topio, Hursula, Yannick Courbois, Emily K. Farran e Pascal Sockeel. "Route learning and shortcut performance in adults with intellectual disability: A study with virtual environments". Research in Developmental Disabilities 32, n.º 1 (janeiro de 2011): 345–52. http://dx.doi.org/10.1016/j.ridd.2010.10.014.
Texto completo da fonteClegg, Benjamin A. "Stimulus-Specific Sequence Representation in Serial Reaction Time Tasks". Quarterly Journal of Experimental Psychology Section A 58, n.º 6 (agosto de 2005): 1087–101. http://dx.doi.org/10.1080/02724980443000485.
Texto completo da fonteSong, Rui, Fausto Giunchiglia, Yingji Li, Mingjie Tian e Hao Xu. "TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 17 (24 de março de 2024): 18999–9007. http://dx.doi.org/10.1609/aaai.v38i17.29866.
Texto completo da fonteSuparjan, Suparjan, e Nining Ismiyani. "The Use of Tanjungpura University’s e-Learning-Moodle LMS during Online Learning: Problems, Solutions and Continuation". Ta'dib 26, n.º 1 (25 de junho de 2023): 71. http://dx.doi.org/10.31958/jt.v26i1.7902.
Texto completo da fonteHusain, Arshi, e Virendra P. Vishvakarma. "Optimized deterministic multikernel extreme learning machine for classification of COVID-19 chest Xray images". Journal of Information and Optimization Sciences 44, n.º 4 (2023): 771–93. http://dx.doi.org/10.47974/jios-1319.
Texto completo da fonteXu, Chendong, Weigang Wang, Yunwei Zhang, Jie Qin, Shujuan Yu e Yun Zhang. "An Indoor Localization System Using Residual Learning with Channel State Information". Entropy 23, n.º 5 (7 de maio de 2021): 574. http://dx.doi.org/10.3390/e23050574.
Texto completo da fonteArun, K., e A. Srinagesh. "Multilingual twitter sentiment analysis using machine learning". International Journal of Electrical and Computer Engineering (IJECE) 10, n.º 6 (1 de dezembro de 2020): 5992. http://dx.doi.org/10.11591/ijece.v10i6.pp5992-6000.
Texto completo da fonteZheng, Hui, Yizhi Cao, Min Sun, Guihai Guo, Junzhen Meng, Xinwei Guo e Yanchi Jiang. "Mixed Structure with 3D Multi-Shortcut-Link Networks for Hyperspectral Image Classification". Remote Sensing 14, n.º 5 (2 de março de 2022): 1230. http://dx.doi.org/10.3390/rs14051230.
Texto completo da fonteHolmberg, Linn. "Right and Wrong Ways of Knowing". 1700-tal: Nordic Journal for Eighteenth-Century Studies 20 (20 de dezembro de 2023): 8–33. http://dx.doi.org/10.7557/4.7203.
Texto completo da fonteSun, Chaoyue, Ruogu Fang, Marco Salemi, Mattia Prosperi e Brittany Rife Magalis. "DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction". PLOS Computational Biology 20, n.º 4 (10 de abril de 2024): e1011351. http://dx.doi.org/10.1371/journal.pcbi.1011351.
Texto completo da fonteThanuja, B. "Machine Learning Based Crime Rate Analysis Using Python". International Journal for Research in Applied Science and Engineering Technology 10, n.º 11 (30 de novembro de 2022): 1312–16. http://dx.doi.org/10.22214/ijraset.2022.47574.
Texto completo da fonteWelte, Peter O. "Caveat Examiner: Beware Clever Students". Perceptual and Motor Skills 77, n.º 3_suppl (dezembro de 1993): 1213–14. http://dx.doi.org/10.2466/pms.1993.77.3f.1213.
Texto completo da fontePollock, Mica. "Flipping Our Scripts about Undocumented Immigration". Genealogy 4, n.º 1 (19 de março de 2020): 29. http://dx.doi.org/10.3390/genealogy4010029.
Texto completo da fontePetrov, Sergei, Tapan Mukerji, Xin Zhang e Xinfei Yan. "Shape Carving Methods of Geologic Body Interpretation from Seismic Data Based on Deep Learning". Energies 15, n.º 3 (31 de janeiro de 2022): 1064. http://dx.doi.org/10.3390/en15031064.
Texto completo da fonteMah, Christopher, Hillary Walker, Lena Phalen, Sarah Levine, Sarah W. Beck e Jaylen Pittman. "Beyond CheatBots: Examining Tensions in Teachers’ and Students’ Perceptions of Cheating and Learning with ChatGPT". Education Sciences 14, n.º 5 (7 de maio de 2024): 500. http://dx.doi.org/10.3390/educsci14050500.
Texto completo da fonteWang, Tong, Yuan Yao, Feng Xu, Miao Xu, Shengwei An e Ting Wang. "Inspecting Prediction Confidence for Detecting Black-Box Backdoor Attacks". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 1 (24 de março de 2024): 274–82. http://dx.doi.org/10.1609/aaai.v38i1.27780.
Texto completo da fonteSulistyo, Totok, e Rohmat Fauzi. "Soil Infiltration Rate Prediction using Machine Learning Regression Model: A Case Study on Sepinggan River Basin, Balikpapan, Indonesia". Indonesian Journal on Geoscience 10, n.º 3 (23 de novembro de 2023): 335–47. http://dx.doi.org/10.17014/ijog.10.3.335-347.
Texto completo da fonteRamsgaard Thomsen, Mette, Paul Nicholas, Martin Tamke, Sebastian Gatz, Yuliya Sinke e Gabriella Rossi. "Towards machine learning for architectural fabrication in the age of industry 4.0". International Journal of Architectural Computing 18, n.º 4 (17 de agosto de 2020): 335–52. http://dx.doi.org/10.1177/1478077120948000.
Texto completo da fonteKURAKAMI, Takeru, Kazuyoshi SOUMA, Takashi MIYAMOTO, Takahiko FURUYA, Jun MAGOME e Hiroshi ISHIDAIRA. "APPLICATION OF A DEEP-LEARNING METHOD INCLUDING SHORTCUT PATHS TO CORRECT THE PRECIPITATION OUTPUTS OF A NUMERICAL WEATHER PREDICTION MODEL". Journal of Japan Society of Civil Engineers, Ser. G (Environmental Research) 76, n.º 5 (2020): I_471—I_478. http://dx.doi.org/10.2208/jscejer.76.5_i_471.
Texto completo da fonteLópez-Cabrera, José Daniel, Rubén Orozco-Morales, Jorge Armando Portal-Díaz, Orlando Lovelle-Enríquez e Marlén Pérez-Díaz. "Current limitations to identify covid-19 using artificial intelligence with chest x-ray imaging (part ii). The shortcut learning problem". Health and Technology 11, n.º 6 (10 de outubro de 2021): 1331–45. http://dx.doi.org/10.1007/s12553-021-00609-8.
Texto completo da fonteZhao, Yu, Rennong Yang, Guillaume Chevalier, Ximeng Xu e Zhenxing Zhang. "Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors". Mathematical Problems in Engineering 2018 (30 de dezembro de 2018): 1–13. http://dx.doi.org/10.1155/2018/7316954.
Texto completo da fonteJ, Kamalakannan, e Chandana Mani R K. "ERNet : Enhanced ResNet for classification of breast histopathological images". ELCVIA Electronic Letters on Computer Vision and Image Analysis 22, n.º 2 (14 de março de 2024): 53–68. http://dx.doi.org/10.5565/rev/elcvia.1614.
Texto completo da fonteHe, Z., H. He, J. Li, M. A. Chapman e H. Ding. "A SHORT-CUT CONNECTIONS-BASED NEURAL NETWORK FOR BUILDING EXTRACTION FROM HIGH RESOLUTION ORTHOIMAGERY". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2022 (30 de maio de 2022): 39–44. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2022-39-2022.
Texto completo da fonteLiu, Yao, Lianru Gao, Chenchao Xiao, Ying Qu, Ke Zheng e Andrea Marinoni. "Hyperspectral Image Classification Based on a Shuffled Group Convolutional Neural Network with Transfer Learning". Remote Sensing 12, n.º 11 (1 de junho de 2020): 1780. http://dx.doi.org/10.3390/rs12111780.
Texto completo da fonteXu, Pengcheng, Zhongyuan Guo, Lei Liang e Xiaohang Xu. "MSF-Net: Multi-Scale Feature Learning Network for Classification of Surface Defects of Multifarious Sizes". Sensors 21, n.º 15 (29 de julho de 2021): 5125. http://dx.doi.org/10.3390/s21155125.
Texto completo da fonteMalik, Sohail Iqbal, Mohanaad Shakir, Abdalla Eldow e Mohammed Waseem Ashfaque. "Promoting Algorithmic Thinking in an Introductory Programming Course". International Journal of Emerging Technologies in Learning (iJET) 14, n.º 01 (17 de janeiro de 2019): 84. http://dx.doi.org/10.3991/ijet.v14i01.9061.
Texto completo da fonteDevaney, Kirsty. "‘Waiting for the wow factor’: Perspectives on computer technology in classroom composing". Journal of Music, Technology & Education 12, n.º 2 (1 de setembro de 2019): 121–39. http://dx.doi.org/10.1386/jmte_00002_1.
Texto completo da fonteXu, Yao, e Qin Yu. "Adaptive Weighted Multi-Level Fusion of Multi-Scale Features: A New Approach to Pedestrian Detection". Future Internet 13, n.º 2 (2 de fevereiro de 2021): 38. http://dx.doi.org/10.3390/fi13020038.
Texto completo da fonteXie, Zhousan, Shikui Tu e Lei Xu. "Multilevel Attention Network with Semi-supervised Domain Adaptation for Drug-Target Prediction". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 1 (24 de março de 2024): 329–37. http://dx.doi.org/10.1609/aaai.v38i1.27786.
Texto completo da fonteMohamed, Islam A., Adel Othman e Mohamed Fathy. "A new approach to improve reservoir modeling via machine learning". Leading Edge 39, n.º 3 (março de 2020): 170–75. http://dx.doi.org/10.1190/tle39030170.1.
Texto completo da fonteZakareya, Salman, Habib Izadkhah e Jaber Karimpour. "A New Deep-Learning-Based Model for Breast Cancer Diagnosis from Medical Images". Diagnostics 13, n.º 11 (1 de junho de 2023): 1944. http://dx.doi.org/10.3390/diagnostics13111944.
Texto completo da fonteHe, Anzheng, Zishuo Dong, Hang Zhang, Allen A. Zhang, Shi Qiu, Yang Liu, Kelvin C. P. Wang e Zhihao Lin. "Automated Pixel-Level Detection of Expansion Joints on Asphalt Pavement Using a Deep-Learning-Based Approach". Structural Control and Health Monitoring 2023 (23 de maio de 2023): 1–15. http://dx.doi.org/10.1155/2023/7552337.
Texto completo da fonteZhang, Xuetao, Kuangang Fan, Haonan Hou e Chuankai Liu. "Real-Time Detection of Drones Using Channel and Layer Pruning, Based on the YOLOv3-SPP3 Deep Learning Algorithm". Micromachines 13, n.º 12 (11 de dezembro de 2022): 2199. http://dx.doi.org/10.3390/mi13122199.
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