Artigos de revistas sobre o tema "Out-of-distribution generalization"
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Ye, Nanyang, Lin Zhu, Jia Wang, Zhaoyu Zeng, Jiayao Shao, Chensheng Peng, Bikang Pan, Kaican Li e Jun Zhu. "Certifiable Out-of-Distribution Generalization". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junho de 2023): 10927–35. http://dx.doi.org/10.1609/aaai.v37i9.26295.
Texto completo da fonteYuan, Lingxiao, Harold S. Park e Emma Lejeune. "Towards out of distribution generalization for problems in mechanics". Computer Methods in Applied Mechanics and Engineering 400 (outubro de 2022): 115569. http://dx.doi.org/10.1016/j.cma.2022.115569.
Texto completo da fonteLiu, Anji, Hongming Xu, Guy Van den Broeck e Yitao Liang. "Out-of-Distribution Generalization by Neural-Symbolic Joint Training". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 10 (26 de junho de 2023): 12252–59. http://dx.doi.org/10.1609/aaai.v37i10.26444.
Texto completo da fonteYu, Yemin, Luotian Yuan, Ying Wei, Hanyu Gao, Fei Wu, Zhihua Wang e Xinhai Ye. "RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Prediction". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 1 (24 de março de 2024): 374–82. http://dx.doi.org/10.1609/aaai.v38i1.27791.
Texto completo da fonteZhu, Lin, Xinbing Wang, Chenghu Zhou e Nanyang Ye. "Bayesian Cross-Modal Alignment Learning for Few-Shot Out-of-Distribution Generalization". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junho de 2023): 11461–69. http://dx.doi.org/10.1609/aaai.v37i9.26355.
Texto completo da fonteLavda, Frantzeska, e Alexandros Kalousis. "Semi-Supervised Variational Autoencoders for Out-of-Distribution Generation". Entropy 25, n.º 12 (14 de dezembro de 2023): 1659. http://dx.doi.org/10.3390/e25121659.
Texto completo da fonteSu, Hang, e Wei Wang. "An Out-of-Distribution Generalization Framework Based on Variational Backdoor Adjustment". Mathematics 12, n.º 1 (26 de dezembro de 2023): 85. http://dx.doi.org/10.3390/math12010085.
Texto completo da fonteCao, Linfeng, Aofan Jiang, Wei Li, Huaying Wu e Nanyang Ye. "OoDHDR-Codec: Out-of-Distribution Generalization for HDR Image Compression". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 1 (28 de junho de 2022): 158–66. http://dx.doi.org/10.1609/aaai.v36i1.19890.
Texto completo da fonteDeng, Bin, e Kui Jia. "Counterfactual Supervision-Based Information Bottleneck for Out-of-Distribution Generalization". Entropy 25, n.º 2 (18 de janeiro de 2023): 193. http://dx.doi.org/10.3390/e25020193.
Texto completo da fonteAshok, Arjun, Chaitanya Devaguptapu e Vineeth N. Balasubramanian. "Learning Modular Structures That Generalize Out-of-Distribution (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 11 (28 de junho de 2022): 12905–6. http://dx.doi.org/10.1609/aaai.v36i11.21589.
Texto completo da fonteZou, Xin, e Weiwei Liu. "Coverage-Guaranteed Prediction Sets for Out-of-Distribution Data". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 15 (24 de março de 2024): 17263–70. http://dx.doi.org/10.1609/aaai.v38i15.29673.
Texto completo da fonteBai, Haoyue, Rui Sun, Lanqing Hong, Fengwei Zhou, Nanyang Ye, Han-Jia Ye, S. H. Gary Chan e Zhenguo Li. "DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 8 (18 de maio de 2021): 6705–13. http://dx.doi.org/10.1609/aaai.v35i8.16829.
Texto completo da fonteFan, Caoyun, Wenqing Chen, Jidong Tian, Yitian Li, Hao He e Yaohui Jin. "Unlock the Potential of Counterfactually-Augmented Data in Out-Of-Distribution Generalization". Expert Systems with Applications 238 (março de 2024): 122066. http://dx.doi.org/10.1016/j.eswa.2023.122066.
Texto completo da fonteRamachandran, Sai Niranjan, Rudrabha Mukhopadhyay, Madhav Agarwal, C. V. Jawahar e Vinay Namboodiri. "Understanding the Generalization of Pretrained Diffusion Models on Out-of-Distribution Data". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 13 (24 de março de 2024): 14767–75. http://dx.doi.org/10.1609/aaai.v38i13.29395.
Texto completo da fonteJia, Tianrui, Haoyang Li, Cheng Yang, Tao Tao e Chuan Shi. "Graph Invariant Learning with Subgraph Co-mixup for Out-of-Distribution Generalization". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 8 (24 de março de 2024): 8562–70. http://dx.doi.org/10.1609/aaai.v38i8.28700.
Texto completo da fonteZhang, Lily H., e Rajesh Ranganath. "Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 12 (26 de junho de 2023): 15305–12. http://dx.doi.org/10.1609/aaai.v37i12.26785.
Texto completo da fonteGwon, Kyungpil, e Joonhyuk Yoo. "Out-of-Distribution (OOD) Detection and Generalization Improved by Augmenting Adversarial Mixup Samples". Electronics 12, n.º 6 (16 de março de 2023): 1421. http://dx.doi.org/10.3390/electronics12061421.
Texto completo da fonteMaier, Anatol, e Christian Riess. "Reliable Out-of-Distribution Recognition of Synthetic Images". Journal of Imaging 10, n.º 5 (1 de maio de 2024): 110. http://dx.doi.org/10.3390/jimaging10050110.
Texto completo da fonteBoccato, Tommaso, Alberto Testolin e Marco Zorzi. "Learning Numerosity Representations with Transformers: Number Generation Tasks and Out-of-Distribution Generalization". Entropy 23, n.º 7 (3 de julho de 2021): 857. http://dx.doi.org/10.3390/e23070857.
Texto completo da fonteChen, Minghui, Cheng Wen, Feng Zheng, Fengxiang He e Ling Shao. "VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 1 (28 de junho de 2022): 321–29. http://dx.doi.org/10.1609/aaai.v36i1.19908.
Texto completo da fonteXin, Shiji, Yifei Wang, Jingtong Su e Yisen Wang. "On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junho de 2023): 10519–27. http://dx.doi.org/10.1609/aaai.v37i9.26250.
Texto completo da fonteHassan, A., S. A. Dar, P. B. Ahmad e B. A. Para. "A new generalization of Aradhana distribution: Properties and applications". Journal of Applied Mathematics, Statistics and Informatics 16, n.º 2 (1 de dezembro de 2020): 51–66. http://dx.doi.org/10.2478/jamsi-2020-0009.
Texto completo da fonteChen, Zhe, Zhiquan Ding, Xiaoling Zhang, Xin Zhang e Tianqi Qin. "Improving Out-of-Distribution Generalization in SAR Image Scene Classification with Limited Training Samples". Remote Sensing 15, n.º 24 (17 de dezembro de 2023): 5761. http://dx.doi.org/10.3390/rs15245761.
Texto completo da fonteSha, Naijun. "A New Inference Approach for Type-II Generalized Birnbaum-Saunders Distribution". Stats 2, n.º 1 (19 de fevereiro de 2019): 148–63. http://dx.doi.org/10.3390/stats2010011.
Texto completo da fonteSharifi-Noghabi, Hossein, Parsa Alamzadeh Harjandi, Olga Zolotareva, Colin C. Collins e Martin Ester. "Out-of-distribution generalization from labelled and unlabelled gene expression data for drug response prediction". Nature Machine Intelligence 3, n.º 11 (novembro de 2021): 962–72. http://dx.doi.org/10.1038/s42256-021-00408-w.
Texto completo da fonteDas, Siddhant, e Markus Nöth. "Times of arrival and gauge invariance". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 477, n.º 2250 (junho de 2021): 20210101. http://dx.doi.org/10.1098/rspa.2021.0101.
Texto completo da fonteZhi Tan, Zhi Tan, e Zhao-Fei Teng Zhi Tan. "Image Domain Generalization Method based on Solving Domain Discrepancy Phenomenon". 電腦學刊 33, n.º 3 (junho de 2022): 171–85. http://dx.doi.org/10.53106/199115992022063303014.
Texto completo da fonteVasiliuk, Anton, Daria Frolova, Mikhail Belyaev e Boris Shirokikh. "Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation". Journal of Imaging 9, n.º 9 (18 de setembro de 2023): 191. http://dx.doi.org/10.3390/jimaging9090191.
Texto completo da fonteBogin, Ben, Sanjay Subramanian, Matt Gardner e Jonathan Berant. "Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering". Transactions of the Association for Computational Linguistics 9 (2021): 195–210. http://dx.doi.org/10.1162/tacl_a_00361.
Texto completo da fonteHe, Rundong, Yue Yuan, Zhongyi Han, Fan Wang, Wan Su, Yilong Yin, Tongliang Liu e Yongshun Gong. "Exploring Channel-Aware Typical Features for Out-of-Distribution Detection". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 11 (24 de março de 2024): 12402–10. http://dx.doi.org/10.1609/aaai.v38i11.29132.
Texto completo da fonteLee, Ingyun, Wooju Lee e Hyun Myung. "Domain Generalization with Vital Phase Augmentation". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 4 (24 de março de 2024): 2892–900. http://dx.doi.org/10.1609/aaai.v38i4.28070.
Texto completo da fonteDing, Kun, Haojian Zhang, Qiang Yu, Ying Wang, Shiming Xiang e Chunhong Pan. "Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt Tuning". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 2 (24 de março de 2024): 1528–36. http://dx.doi.org/10.1609/aaai.v38i2.27918.
Texto completo da fonteSimmachan, Teerawat, e Wikanda Phaphan. "Generalization of Two-Sided Length Biased Inverse Gaussian Distributions and Applications". Symmetry 14, n.º 10 (20 de setembro de 2022): 1965. http://dx.doi.org/10.3390/sym14101965.
Texto completo da fonteNain, Philippe. "On a generalization of the preemptive resume priority". Advances in Applied Probability 18, n.º 1 (março de 1986): 255–73. http://dx.doi.org/10.2307/1427245.
Texto completo da fonteNain, Philippe. "On a generalization of the preemptive resume priority". Advances in Applied Probability 18, n.º 01 (março de 1986): 255–73. http://dx.doi.org/10.1017/s0001867800015652.
Texto completo da fonteZhang, Weifeng, Zhiyuan Wang, Kunpeng Zhang, Ting Zhong e Fan Zhou. "DyCVAE: Learning Dynamic Causal Factors for Non-stationary Series Domain Generalization (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junho de 2023): 16382–83. http://dx.doi.org/10.1609/aaai.v37i13.27051.
Texto completo da fonteChen, Zhengyu, Teng Xiao, Kun Kuang, Zheqi Lv, Min Zhang, Jinluan Yang, Chengqiang Lu, Hongxia Yang e Fei Wu. "Learning to Reweight for Generalizable Graph Neural Network". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 8 (24 de março de 2024): 8320–28. http://dx.doi.org/10.1609/aaai.v38i8.28673.
Texto completo da fonteWelleck, Sean, Peter West, Jize Cao e Yejin Choi. "Symbolic Brittleness in Sequence Models: On Systematic Generalization in Symbolic Mathematics". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 8 (28 de junho de 2022): 8629–37. http://dx.doi.org/10.1609/aaai.v36i8.20841.
Texto completo da fonteNassar, Mazen, Sanku Dey e Devendra Kumar. "Logarithm Transformed Lomax Distribution with Applications". Calcutta Statistical Association Bulletin 70, n.º 2 (novembro de 2018): 122–35. http://dx.doi.org/10.1177/0008068318808135.
Texto completo da fonteLotfollahi, Mohammad, Mohsen Naghipourfar, Fabian J. Theis e F. Alexander Wolf. "Conditional out-of-distribution generation for unpaired data using transfer VAE". Bioinformatics 36, Supplement_2 (dezembro de 2020): i610—i617. http://dx.doi.org/10.1093/bioinformatics/btaa800.
Texto completo da fonteReyes, Jimmy, Mario A. Rojas e Jaime Arrué. "A New Generalization of the Student’s t Distribution with an Application in Quantile Regression". Symmetry 13, n.º 12 (17 de dezembro de 2021): 2444. http://dx.doi.org/10.3390/sym13122444.
Texto completo da fonteMirzadeh, Saeed, e Anis Iranmanesh. "A new class of skew-logistic distribution". Mathematical Sciences 13, n.º 4 (5 de outubro de 2019): 375–85. http://dx.doi.org/10.1007/s40096-019-00306-8.
Texto completo da fonteNeeleman, Ad, e Kriszta Szendrői. "Radical Pro Drop and the Morphology of Pronouns". Linguistic Inquiry 38, n.º 4 (outubro de 2007): 671–714. http://dx.doi.org/10.1162/ling.2007.38.4.671.
Texto completo da fonteet al., Hassan. "A new generalization of the inverse Lomax distribution with statistical properties and applications". International Journal of ADVANCED AND APPLIED SCIENCES 8, n.º 4 (abril de 2021): 89–97. http://dx.doi.org/10.21833/ijaas.2021.04.011.
Texto completo da fonteLi, Dasen, Zhendong Yin, Yanlong Zhao, Wudi Zhao e Jiqing Li. "MLFAnet: A Tomato Disease Classification Method Focusing on OOD Generalization". Agriculture 13, n.º 6 (29 de maio de 2023): 1140. http://dx.doi.org/10.3390/agriculture13061140.
Texto completo da fonteXu, Xiaofeng, Ivor W. Tsang e Chuancai Liu. "Improving Generalization via Attribute Selection on Out-of-the-Box Data". Neural Computation 32, n.º 2 (fevereiro de 2020): 485–514. http://dx.doi.org/10.1162/neco_a_01256.
Texto completo da fonteWurmbrand, Susi. "Stripping and Topless Complements". Linguistic Inquiry 48, n.º 2 (abril de 2017): 341–66. http://dx.doi.org/10.1162/ling_a_00245.
Texto completo da fonteYu, Shujian. "The Analysis of Deep Neural Networks by Information Theory: From Explainability to Generalization". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junho de 2023): 15462. http://dx.doi.org/10.1609/aaai.v37i13.26829.
Texto completo da fonteYu, Runpeng, Hong Zhu, Kaican Li, Lanqing Hong, Rui Zhang, Nanyang Ye, Shao-Lun Huang e Xiuqiang He. "Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 8 (28 de junho de 2022): 8945–53. http://dx.doi.org/10.1609/aaai.v36i8.20877.
Texto completo da fonteSinha, Samarth, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg e Florian Shkurti. "DIBS: Diversity Inducing Information Bottleneck in Model Ensembles". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 11 (18 de maio de 2021): 9666–74. http://dx.doi.org/10.1609/aaai.v35i11.17163.
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