Journal articles on the topic 'Unbiased Learning'
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Ai, Qingyao, Tao Yang, Huazheng Wang, and Jiaxin Mao. "Unbiased Learning to Rank." ACM Transactions on Information Systems 39, no. 2 (March 2021): 1–29. http://dx.doi.org/10.1145/3439861.
Full textVydiswaran, V. G. Vinod, ChengXiang Zhai, Dan Roth, and Peter Pirolli. "Unbiased learning of controversial topics." Proceedings of the American Society for Information Science and Technology 49, no. 1 (2012): 1–4. http://dx.doi.org/10.1002/meet.14504901291.
Full textBackus, B. T. "Optimal learning rates for unbiased perception." Journal of Vision 3, no. 9 (March 16, 2010): 175. http://dx.doi.org/10.1167/3.9.175.
Full textBreeden, Joseph L., and Eugenia Leonova. "Creating Unbiased Machine Learning Models by Design." Journal of Risk and Financial Management 14, no. 11 (November 22, 2021): 565. http://dx.doi.org/10.3390/jrfm14110565.
Full textLi, Ming, Shuo Zhu, Chunxu Li, and Wencang Zhao. "Target unbiased meta-learning for graph classification." Journal of Computational Design and Engineering 8, no. 5 (September 15, 2021): 1355–66. http://dx.doi.org/10.1093/jcde/qwab050.
Full textJia, Zhen, Zhang Zhang, Liang Wang, Caifeng Shan, and Tieniu Tan. "Deep Unbiased Embedding Transfer for Zero-Shot Learning." IEEE Transactions on Image Processing 29 (2020): 1958–71. http://dx.doi.org/10.1109/tip.2019.2947780.
Full textWang, Zong-Hui, Zi-Qian Lu, and Zhe-Ming Lu. "Unbiased hybrid generation network for zero-shot learning." Electronics Letters 56, no. 18 (September 3, 2020): 929–31. http://dx.doi.org/10.1049/el.2020.1594.
Full textShamsi, Zahra, and Diwakar Shukla. "Efficient Unbiased Sampling of Protein Dynamics using Reinforcement Learning." Biophysical Journal 114, no. 3 (February 2018): 673a. http://dx.doi.org/10.1016/j.bpj.2017.11.3630.
Full textPremo, L. S., and Jonathan B. Scholnick. "The Spatial Scale of Social Learning Affects Cultural Diversity." American Antiquity 76, no. 1 (January 2011): 163–76. http://dx.doi.org/10.7183/0002-7316.76.1.163.
Full textGuo, Fan, Weiqing Li, Ziqi Shen, and Xiangyu Shi. "MTCLF: A multitask curriculum learning framework for unbiased glaucoma screenings." Computer Methods and Programs in Biomedicine 221 (June 2022): 106910. http://dx.doi.org/10.1016/j.cmpb.2022.106910.
Full textLv, Fengmao, Haiyang Liu, Yichen Wang, Jiayi Zhao, and Guowu Yang. "Learning Unbiased Zero-Shot Semantic Segmentation Networks Via Transductive Transfer." IEEE Signal Processing Letters 27 (2020): 1640–44. http://dx.doi.org/10.1109/lsp.2020.3023340.
Full textDimitrakakis, Christos, Guangliang Li, and Nikoalos Tziortziotis. "The Reinforcement Learning Competition 2014." AI Magazine 35, no. 3 (September 19, 2014): 61–65. http://dx.doi.org/10.1609/aimag.v35i3.2548.
Full textPan, Yonghua, Zechao Li, Liyan Zhang, and Jinhui Tang. "Causal Inference with Knowledge Distilling and Curriculum Learning for Unbiased VQA." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 3 (August 31, 2022): 1–23. http://dx.doi.org/10.1145/3487042.
Full textLu, Alex, Oren Kraus, Sam Cooper, and Alan Moses. "Learning Biology Through Puzzle-solving: Unbiased Automatic Understanding of Microscopy Images with Self-supervised Learning." Microscopy and Microanalysis 26, S2 (July 30, 2020): 690–92. http://dx.doi.org/10.1017/s1431927620015548.
Full textNguyen, Thanh-Tung, Joshua Zhexue Huang, and Thuy Thi Nguyen. "Unbiased Feature Selection in Learning Random Forests for High-Dimensional Data." Scientific World Journal 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/471371.
Full textMurari, A., J. Vega, G. A. Rattá, G. Vagliasindi, M. F. Johnson, and S. H. Hong. "Unbiased and non-supervised learning methods for disruption prediction at JET." Nuclear Fusion 49, no. 5 (April 29, 2009): 055028. http://dx.doi.org/10.1088/0029-5515/49/5/055028.
Full textBey, Romain, Romain Goussault, François Grolleau, Mehdi Benchoufi, and Raphaël Porcher. "Fold-stratified cross-validation for unbiased and privacy-preserving federated learning." Journal of the American Medical Informatics Association 27, no. 8 (July 4, 2020): 1244–51. http://dx.doi.org/10.1093/jamia/ocaa096.
Full textRizhinashvili, Davit, Abdallah Hussein Sham, and Gholamreza Anbarjafari. "Gender Neutralisation for Unbiased Speech Synthesising." Electronics 11, no. 10 (May 17, 2022): 1594. http://dx.doi.org/10.3390/electronics11101594.
Full textGu, Jeongmin, Jose A. Iglesias-Guitian, and Bochang Moon. "Neural James-Stein Combiner for Unbiased and Biased Renderings." ACM Transactions on Graphics 41, no. 6 (November 30, 2022): 1–14. http://dx.doi.org/10.1145/3550454.3555496.
Full textJaveed, Ashir, Ana Luiza Dallora, Johan Sanmartin Berglund, and Peter Anderberg. "An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning." Life 12, no. 7 (July 21, 2022): 1097. http://dx.doi.org/10.3390/life12071097.
Full textZANIN, DIEGO, and RICCARDO ZECCHINA. "LEARNING INTERFERENCE REDUCTION IN NEURAL NETWORKS." Modern Physics Letters B 09, no. 18 (August 10, 1995): 1165–74. http://dx.doi.org/10.1142/s0217984995001169.
Full textXue, Tianfang, and Haibin Yu. "Unbiased Model-Agnostic Metalearning Algorithm for Learning Target-Driven Visual Navigation Policy." Computational Intelligence and Neuroscience 2021 (December 8, 2021): 1–12. http://dx.doi.org/10.1155/2021/5620751.
Full textHatch, Nile W. "Biased and Unbiased Specification of the Learning Curve: Coping with Unobserved History." Academy of Management Proceedings 2016, no. 1 (January 2016): 18177. http://dx.doi.org/10.5465/ambpp.2016.18177abstract.
Full textDanishvar, Morad, Alireza Mousavi, and Peter Broomhead. "EventiC: A Real-Time Unbiased Event-Based Learning Technique for Complex Systems." IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, no. 5 (May 2020): 1649–62. http://dx.doi.org/10.1109/tsmc.2017.2775666.
Full textTran-Nguyen, Viet-Khoa, Célien Jacquemard, and Didier Rognan. "LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening." Journal of Chemical Information and Modeling 60, no. 9 (April 13, 2020): 4263–73. http://dx.doi.org/10.1021/acs.jcim.0c00155.
Full textAlahmari, Saeed S., Dmitry Goldgof, Lawrence Hall, Hady Ahmady Phoulady, Raj H. Patel, and Peter R. Mouton. "Automated Cell Counts on Tissue Sections by Deep Learning and Unbiased Stereology." Journal of Chemical Neuroanatomy 96 (March 2019): 94–101. http://dx.doi.org/10.1016/j.jchemneu.2018.12.010.
Full textSugiyama, Masashi, and Hidemitsu Ogawa. "Subspace Information Criterion for Model Selection." Neural Computation 13, no. 8 (August 1, 2001): 1863–89. http://dx.doi.org/10.1162/08997660152469387.
Full textHulle, Marc M. Van. "Differential Log Likelihood for Evaluating and Learning Gaussian Mixtures." Neural Computation 18, no. 2 (February 1, 2006): 430–45. http://dx.doi.org/10.1162/089976606775093873.
Full textPritchett, Lant, and Justin Sandefur. "Learning from Experiments when Context Matters." American Economic Review 105, no. 5 (May 1, 2015): 471–75. http://dx.doi.org/10.1257/aer.p20151016.
Full textJain, Shantanu, Justin Delano, Himanshu Sharma, and Predrag Radivojac. "Class Prior Estimation with Biased Positives and Unlabeled Examples." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4255–63. http://dx.doi.org/10.1609/aaai.v34i04.5848.
Full textAllen, Angier, Samson Mataraso, Anna Siefkas, Hoyt Burdick, Gregory Braden, R. Phillip Dellinger, Andrea McCoy, et al. "A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study." JMIR Public Health and Surveillance 6, no. 4 (October 22, 2020): e22400. http://dx.doi.org/10.2196/22400.
Full textAbedin, Md Joynul, Titon Barua, Mahdokht Shaibani, and Mainak Majumder. "A High Throughput and Unbiased Machine Learning Approach for Classification of Graphene Dispersions." Advanced Science 7, no. 20 (August 25, 2020): 2001600. http://dx.doi.org/10.1002/advs.202001600.
Full textLiu, Zhe, Yun Li, Lina Yao, Xianzhi Wang, and Guodong Long. "Task Aligned Generative Meta-learning for Zero-shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 8723–31. http://dx.doi.org/10.1609/aaai.v35i10.17057.
Full textNagao, Yukiko, Mika Sakamoto, Takumi Chinen, Yasushi Okada, and Daisuke Takao. "Robust classification of cell cycle phase and biological feature extraction by image-based deep learning." Molecular Biology of the Cell 31, no. 13 (June 15, 2020): 1346–54. http://dx.doi.org/10.1091/mbc.e20-03-0187.
Full textPereira, João P. B., Erik S. G. Stroes, Aeilko H. Zwinderman, and Evgeni Levin. "Covered Information Disentanglement: Model Transparency via Unbiased Permutation Importance." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7984–92. http://dx.doi.org/10.1609/aaai.v36i7.20769.
Full textWang, Yu-Chen, Yu-Ting Wu, Tzu-Mao Li, and Yung-Yu Chuang. "Learning to cluster for rendering with many lights." ACM Transactions on Graphics 40, no. 6 (December 2021): 1–10. http://dx.doi.org/10.1145/3478513.3480561.
Full textPearl, Lisa S. "When Unbiased Probabilistic Learning Is Not Enough: Acquiring a Parametric System of Metrical Phonology." Language Acquisition 18, no. 2 (April 6, 2011): 87–120. http://dx.doi.org/10.1080/10489223.2011.554261.
Full textXu, Chundong, Qinglin Li, and Dongwen Ying. "An Effective Adaptive Combination Strategy for Distributed Learning Network." Applied Sciences 11, no. 12 (June 20, 2021): 5723. http://dx.doi.org/10.3390/app11125723.
Full textChristensen, Anders S., and O. Anatole von Lilienfeld. "Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties." CHIMIA International Journal for Chemistry 73, no. 12 (December 18, 2019): 1028–31. http://dx.doi.org/10.2533/chimia.2019.1028.
Full textYang, QW. "A new biased estimation method based on Neumann series for solving ill-posed problems." International Journal of Advanced Robotic Systems 16, no. 4 (July 2019): 172988141987205. http://dx.doi.org/10.1177/1729881419872058.
Full textVillarreal, Micah N., Alexander J. Kamrud, and Brett J. Borghetti. "Confirmation Bias Estimation from Electroencephalography with Machine Learning." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 73–77. http://dx.doi.org/10.1177/1071181319631208.
Full textWENDEMUTH, A., and D. SHERRINGTON. "FAST LEARNING OF BIASED PATTERNS IN NEURAL NETWORKS." International Journal of Neural Systems 04, no. 03 (September 1993): 223–30. http://dx.doi.org/10.1142/s0129065793000183.
Full textFreed, Benjamin, Guillaume Sartoretti, Jiaheng Hu, and Howie Choset. "Communication Learning via Backpropagation in Discrete Channels with Unknown Noise." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7160–68. http://dx.doi.org/10.1609/aaai.v34i05.6205.
Full textSollich, Peter, and David Barber. "Online Learning from Finite Training Sets and Robustness to Input Bias." Neural Computation 10, no. 8 (November 1, 1998): 2201–17. http://dx.doi.org/10.1162/089976698300017034.
Full textZhu, Beier, Yulei Niu, Xian-Sheng Hua, and Hanwang Zhang. "Cross-Domain Empirical Risk Minimization for Unbiased Long-Tailed Classification." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 3589–97. http://dx.doi.org/10.1609/aaai.v36i3.20271.
Full textZhang, Yikai, Hui Qu, Dimitris Metaxas, and Chao Chen. "Local Regularizer Improves Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6861–68. http://dx.doi.org/10.1609/aaai.v34i04.6167.
Full textHochberg, Judith G. "First steps in the acquisition of Spanish stress." Journal of Child Language 15, no. 2 (June 1988): 273–92. http://dx.doi.org/10.1017/s030500090001237x.
Full textSugiyama, Masashi, Motoaki Kawanabe, and Klaus-Robert Müller. "Trading Variance Reduction with Unbiasedness: The Regularized Subspace Information Criterion for Robust Model Selection in Kernel Regression." Neural Computation 16, no. 5 (May 1, 2004): 1077–104. http://dx.doi.org/10.1162/089976604773135113.
Full textWilson, Scott R., Murray E. Close, Phillip Abraham, Theo S. Sarris, Laura Banasiak, Roland Stenger, and John Hadfield. "Achieving unbiased predictions of national-scale groundwater redox conditions via data oversampling and statistical learning." Science of The Total Environment 705 (February 2020): 135877. http://dx.doi.org/10.1016/j.scitotenv.2019.135877.
Full textHuang, Yiyan, Cheuk Hang Leung, Xing Yan, Qi Wu, Nanbo Peng, Dongdong Wang, and Zhixiang Huang. "The Causal Learning of Retail Delinquency." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 204–12. http://dx.doi.org/10.1609/aaai.v35i1.16094.
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