Gotowa bibliografia na temat „Novel task transfer”
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Artykuły w czasopismach na temat "Novel task transfer"
Vincent, Vercruyssen, Meert Wannes i Davis Jesse. "Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 04 (3.04.2020): 6054–61. http://dx.doi.org/10.1609/aaai.v34i04.6068.
Pełny tekst źródłaKearney, Philip E., i Phil Judge. "Successful Transfer of a Motor Learning Strategy to a Novel Sport". Perceptual and Motor Skills 124, nr 5 (7.07.2017): 1009–21. http://dx.doi.org/10.1177/0031512517719189.
Pełny tekst źródłaCatrambone, Richard. "Specific versus General Instructions: Initial Performance and Later Transfer". Proceedings of the Human Factors Society Annual Meeting 33, nr 19 (październik 1989): 1320–23. http://dx.doi.org/10.1177/154193128903301918.
Pełny tekst źródłaGARCÍA, ESTEBAN O., ENRIQUE MUNOZ DE COTE i EDUARDO F. MORALES. "TRANSFER LEARNING FOR CONTINUOUS STATE AND ACTION SPACES". International Journal of Pattern Recognition and Artificial Intelligence 28, nr 07 (14.10.2014): 1460007. http://dx.doi.org/10.1142/s0218001414600076.
Pełny tekst źródłaKono, Hitoshi, Yuto Sakamoto, Yonghoon Ji i Hiromitsu Fujii. "Automatic Transfer Rate Adjustment for Transfer Reinforcement Learning". International Journal of Artificial Intelligence & Applications 11, nr 6 (30.11.2020): 47–54. http://dx.doi.org/10.5121/ijaia.2020.11605.
Pełny tekst źródłaAbdelrahman, Amro M., Denny Yu, Bethany R. Lowndes, EeeLN H. Buckarma, Becca L. Gas, David R. Farley, Juliane Bingener i M. Susan Hallbeck. "Validation of a Novel Inverted Peg Transfer Task: Advancing Beyond the Regular Peg Transfer Task for Surgical Simulation-Based Assessment". Journal of Surgical Education 75, nr 3 (maj 2018): 836–43. http://dx.doi.org/10.1016/j.jsurg.2017.09.028.
Pełny tekst źródłaLei, Feifei, Jieren Cheng, Yue Yang, Xiangyan Tang, Victor S. Sheng i Chunzao Huang. "Improving Heterogeneous Network Knowledge Transfer Based on the Principle of Generative Adversarial". Electronics 10, nr 13 (24.06.2021): 1525. http://dx.doi.org/10.3390/electronics10131525.
Pełny tekst źródłaJing, Mingxuan, Xiaojian Ma, Wenbing Huang, Fuchun Sun i Huaping Liu. "Task Transfer by Preference-Based Cost Learning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 2471–78. http://dx.doi.org/10.1609/aaai.v33i01.33012471.
Pełny tekst źródłaToader, Andra F., i Thomas Kessler. "Task Variation and Mental Models Divergence Influencing the Transfer of Team Learning". Small Group Research 49, nr 5 (26.07.2018): 545–75. http://dx.doi.org/10.1177/1046496418786429.
Pełny tekst źródłaVandenbroucke, B., i P. Camps. "CMACIONIZE 2.0: a novel task-based approach to Monte Carlo radiation transfer". Astronomy & Astrophysics 641 (wrzesień 2020): A66. http://dx.doi.org/10.1051/0004-6361/202038364.
Pełny tekst źródłaRozprawy doktorskie na temat "Novel task transfer"
Wrathall, Stephen, i res cand@acu edu au. "The Effects of Contextual Interference and Variability of Practice on the Acquisition of a Motor Task and Transfer to a Novel Task". Australian Catholic University. School of Exercise Science, 2004. http://dlibrary.acu.edu.au/digitaltheses/public/adt-acuvp63.29082005.
Pełny tekst źródłaYe, Meng. "VISUAL AND SEMANTIC KNOWLEDGE TRANSFER FOR NOVEL TASKS". Diss., Temple University Libraries, 2019. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/583037.
Pełny tekst źródłaPh.D.
Data is a critical component in a supervised machine learning system. Many successful applications of learning systems on various tasks are based on a large amount of labeled data. For example, deep convolutional neural networks have surpassed human performance on ImageNet classification, which consists of millions of labeled images. However, one challenge in conventional supervised learning systems is their generalization ability. Once a model is trained on a specific dataset, it can only perform the task on those \emph{seen} classes and cannot be used for novel \emph{unseen} classes. In order to make the model work on new classes, one has to collect and label new data and then re-train the model. However, collecting data and labeling them is labor-intensive and costly, in some cases, it is even impossible. Also, there is an enormous amount of different tasks in the real world. It is not applicable to create a dataset for each of them. These problems raise the need for Transfer Learning, which is aimed at using data from the \emph{source} domain to improve the performance of a model on the \emph{target} domain, and these two domains have different data or different tasks. One specific case of transfer learning is Zero-Shot Learning. It deals with the situation where \emph{source} domain and \emph{target} domain have the same data distribution but do not have the same set of classes. For example, a model is given animal images of `cat' and `dog' for training and will be tested on classifying 'tiger' and 'wolf' images, which it has never seen. Different from conventional supervised learning, Zero-Shot Learning does not require training data in the \emph{target} domain to perform classification. This property gives ZSL the potential to be broadly applied in various applications where a system is expected to tackle unexpected situations. In this dissertation, we develop algorithms that can help a model effectively transfer visual and semantic knowledge learned from \emph{source} task to \emph{target} task. More specifically, first we develop a model that learns a uniform visual representation of semantic attributes, which help alleviate the domain shift problem in Zero-Shot Learning. Second, we develop an ensemble network architecture with a progressive training scheme, which transfers \emph{source} domain knowledge to the \emph{target} domain in an end-to-end manner. Lastly, we move a step beyond ZSL and explore Label-less Classification, which transfers knowledge from pre-trained object detectors into scene classification tasks. Our label-less classification takes advantage of word embeddings trained from unorganized online text, thus eliminating the need for expert-defined semantic attributes for each class. Through comprehensive experiments, we show that the proposed methods can effectively transfer visual and semantic knowledge between tasks, and achieve state-of-the-art performances on standard datasets.
Temple University--Theses
Chan, Sharon. "Far-transfer effects of working memory training on a novel problem solving task". Thesis, 2014. http://hdl.handle.net/1828/5508.
Pełny tekst źródłaGraduate
0620
0633
sharonc@uvic.ca
Książki na temat "Novel task transfer"
Akande, Dapo, Jaakko Kuosmanen, Helen McDermott i Dominic Roser, red. Human Rights and 21st Century Challenges. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198824770.001.0001.
Pełny tekst źródłaCzęści książek na temat "Novel task transfer"
Al-Habaibeh, Amin, Ampea Boateng i Hyunjoo Lee. "Innovative Strategy for Addressing the Challenges of Monitoring Off-Shore Wind Turbines for Condition-Based Maintenance". W Springer Proceedings in Energy, 189–96. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_24.
Pełny tekst źródłaPassingham, Richard E. "Prefrontal Cortex". W Understanding the Prefrontal Cortex, 287–330. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198844570.003.0008.
Pełny tekst źródłaDuy, Phan The, Nghi Hoang Khoa, Hoang Hiep, Nguyen Ba Tuan, Hien Do Hoang, Do Thi Thu Hien i Van-Hau Pham. "A Deep Transfer Learning Approach for Flow-Based Intrusion Detection in SDN-Enabled Network". W Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210031.
Pełny tekst źródłaKuchinsky, Stefanie E., i Henk J. Haarmann. "Neuroscience Perspectives on Cognitive Training". W Cognitive and Working Memory Training, 79–104. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780199974467.003.0005.
Pełny tekst źródłaSas, Corina. "Sense of Presence". W Encyclopedia of Human Computer Interaction, 511–17. IGI Global, 2006. http://dx.doi.org/10.4018/978-1-59140-562-7.ch076.
Pełny tekst źródłaDaisy, Anjali. "Knowledge Graph Generation". W Advances in Computer and Electrical Engineering, 115–21. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1159-6.ch007.
Pełny tekst źródłaHäkkilä, Jonna, i Jenine Beekhuyzen. "Using Mobile Communication Technology in Student Mentoring". W Encyclopedia of Human Computer Interaction, 680–85. IGI Global, 2006. http://dx.doi.org/10.4018/978-1-59140-562-7.ch102.
Pełny tekst źródłaHäkkilä, Jonna, i Jenine Beekhuyzen. "Using Mobile Communication Technology in Student Mentoring". W Mobile Computing, 1351–58. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-054-7.ch111.
Pełny tekst źródłaStreszczenia konferencji na temat "Novel task transfer"
Um, Terry Taewoong, Myoung Soo Park i Jung-Min Park. "Independent Joint Learning: A novel task-to-task transfer learning scheme for robot models". W 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2014. http://dx.doi.org/10.1109/icra.2014.6907694.
Pełny tekst źródłaSoh, Harold, Shu Pan, Min Chen i David Hsu. "Trust Dynamics and Transfer across Human-Robot Interaction Tasks: Bayesian and Neural Computational Models". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/868.
Pełny tekst źródłaZheng, Zimu, Yuqi Wang, Quanyu Dai, Huadi Zheng i Dan Wang. "Metadata-driven Task Relation Discovery for Multi-task Learning". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/615.
Pełny tekst źródłaZhu, Mingrui, Nannan Wang, Xinbo Gao, Jie Li i Zhifeng Li. "Face Photo-Sketch Synthesis via Knowledge Transfer". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/147.
Pełny tekst źródłaFang, Yuchun, Zhengyan Ma, Zhaoxiang Zhang, Xu-Yao Zhang i Xiang Bai. "Dynamic Multi-Task Learning with Convolutional Neural Network". W Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/231.
Pełny tekst źródłaTian, Qiangxing, Guanchu Wang, Jinxin Liu, Donglin Wang i Yachen Kang. "Independent Skill Transfer for Deep Reinforcement Learning". W Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/401.
Pełny tekst źródłaLee, Seungwon, James Stokes i Eric Eaton. "Learning Shared Knowledge for Deep Lifelong Learning using Deconvolutional Networks". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/393.
Pełny tekst źródłaWu, Qianhui, Zijia Lin, Börje F. Karlsson, Biqing Huang i Jian-Guang Lou. "UniTrans : Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data". W Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/543.
Pełny tekst źródłaGuo, Yuchen, Guiguang Ding, Jungong Han i Yue Gao. "SitNet: Discrete Similarity Transfer Network for Zero-shot Hashing". W Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/245.
Pełny tekst źródłaLiu, Ziyu, Wei Shao, Jie Zhang, Min Zhang i Kun Huang. "Transfer Learning via Optimal Transportation for Integrative Cancer Patient Stratification". W Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/380.
Pełny tekst źródłaRaporty organizacyjne na temat "Novel task transfer"
Jozewicz, Wojciech, i G. T. Rochelle. Theoretical approach for enhanced mass transfer effects in duct flue gas desulfurization processes. Topical report for Task 4, Novel techniques. Office of Scientific and Technical Information (OSTI), wrzesień 1991. http://dx.doi.org/10.2172/10125937.
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