Literatura científica selecionada sobre o tema "Out-of-distribution generalization"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Índice
Consulte a lista de atuais artigos, livros, teses, anais de congressos e outras fontes científicas relevantes para o tema "Out-of-distribution generalization".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Artigos de revistas sobre o assunto "Out-of-distribution generalization"
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 fonteTeses / dissertações sobre o assunto "Out-of-distribution generalization"
Kirchmeyer, Matthieu. "Out-of-distribution Generalization in Deep Learning : Classification and Spatiotemporal Forecasting". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS080.
Texto completo da fonteDeep learning has emerged as a powerful approach for modelling static data like images and more recently for modelling dynamical systems like those underlying times series, videos or physical phenomena. Yet, neural networks were observed to not generalize well outside the training distribution, in other words out-of-distribution. This lack of generalization limits the deployment of deep learning in autonomous systems or online production pipelines, which are faced with constantly evolving data. In this thesis, we design new strategies for out-of-distribution generalization. These strategies handle the specific challenges posed by two main application tasks, classification of static data and spatiotemporal dynamics forecasting. The first two parts of this thesis consider the classification problem. We first investigate how we can efficiently leverage some observed training data from a target domain for adaptation. We then explore how to generalize to unobserved domains without access to such data. The last part of this thesis handles various generalization problems specific to spatiotemporal forecasting
Livros sobre o assunto "Out-of-distribution generalization"
Zabrodin, Anton. Financial applications of random matrix theory: a short review. Editado por Gernot Akemann, Jinho Baik e Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.40.
Texto completo da fonteJames, Philip. The Biology of Urban Environments. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198827238.001.0001.
Texto completo da fonteCapítulos de livros sobre o assunto "Out-of-distribution generalization"
Chen, Zining, Weiqiu Wang, Zhicheng Zhao, Aidong Men e Hong Chen. "Bag of Tricks for Out-of-Distribution Generalization". In Lecture Notes in Computer Science, 465–76. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25075-0_31.
Texto completo da fonteMoruzzi, Caterina. "Toward Out-of-Distribution Generalization Through Inductive Biases". In Studies in Applied Philosophy, Epistemology and Rational Ethics, 57–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09153-7_5.
Texto completo da fonteLi, Dongqi, Zhu Teng, Qirui Li e Ziyin Wang. "Sharpness-Aware Minimization for Out-of-Distribution Generalization". In Communications in Computer and Information Science, 555–67. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8126-7_43.
Texto completo da fonteWang, Fawu, Kang Zhang, Zhengyu Liu, Xia Yuan e Chunxia Zhao. "Deep Relevant Feature Focusing for Out-of-Distribution Generalization". In Pattern Recognition and Computer Vision, 245–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18907-4_19.
Texto completo da fonteWang, Yuqing, Xiangxian Li, Zhuang Qi, Jingyu Li, Xuelong Li, Xiangxu Meng e Lei Meng. "Meta-Causal Feature Learning for Out-of-Distribution Generalization". In Lecture Notes in Computer Science, 530–45. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25075-0_36.
Texto completo da fonteYu, Haoran, Baodi Liu, Yingjie Wang, Kai Zhang, Dapeng Tao e Weifeng Liu. "A Stable Vision Transformer for Out-of-Distribution Generalization". In Pattern Recognition and Computer Vision, 328–39. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8543-2_27.
Texto completo da fonteZhang, Xingxuan, Yue He, Tan Wang, Jiaxin Qi, Han Yu, Zimu Wang, Jie Peng et al. "NICO Challenge: Out-of-Distribution Generalization for Image Recognition Challenges". In Lecture Notes in Computer Science, 433–50. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25075-0_29.
Texto completo da fonteLong, Xi, Ying Cheng, Xiao Mu, Lian Liu e Jingxin Liu. "Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge". In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 73–76. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_11.
Texto completo da fonteJahanifar, Mostafa, Adam Shepard, Neda Zamanitajeddin, R. M. Saad Bashir, Mohsin Bilal, Syed Ali Khurram, Fayyaz Minhas e Nasir Rajpoot. "Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge". In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 48–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_6.
Texto completo da fonteWang, Jiahao, Hao Wang, Zhuojun Dong, Hua Yang, Yuting Yang, Qianyue Bao, Fang Liu e LiCheng Jiao. "A Three-Stage Model Fusion Method for Out-of-Distribution Generalization". In Lecture Notes in Computer Science, 488–99. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25075-0_33.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Out-of-distribution generalization"
Wang, Fawu, Ruizhe Li, Kang Zhang, Xia Yuan e Chunxia Zhao. "Data Distribution Transfer for Out Of Distribution Generalization". In 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2022. http://dx.doi.org/10.1109/mmsp55362.2022.9949199.
Texto completo da fonteWang, Ruoyu, Mingyang Yi, Zhitang Chen e Shengyu Zhu. "Out-of-distribution Generalization with Causal Invariant Transformations". In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.00047.
Texto completo da fonteDeng, Xun, Wenjie Wang, Fuli Feng, Hanwang Zhang, Xiangnan He e Yong Liao. "Counterfactual Active Learning for Out-of-Distribution Generalization". In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-long.636.
Texto completo da fonteZhang, Xingxuan, Peng Cui, Renzhe Xu, Linjun Zhou, Yue He e Zheyan Shen. "Deep Stable Learning for Out-Of-Distribution Generalization". In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00533.
Texto completo da fonteWu, Qitian, Fan Nie, Chenxiao Yang, Tianyi Bao e Junchi Yan. "Graph Out-of-Distribution Generalization via Causal Intervention". In WWW '24: The ACM Web Conference 2024. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3589334.3645604.
Texto completo da fonteKamani, Mohammad Mahdi, Sadegh Farhang, Mehrdad Mahdavi e James Z. Wang. "Targeted Data-driven Regularization for Out-of-Distribution Generalization". In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3403131.
Texto completo da fonteWang, Xin, Peng Cui e Wenwu Zhu. "Out-of-distribution Generalization and Its Applications for Multimedia". In MM '21: ACM Multimedia Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3474085.3478876.
Texto completo da fonteMiao, Qiaowei, Junkun Yuan, Shengyu Zhang, Fei Wu e Kun Kuang. "Domaindiff: Boost out-of-Distribution Generalization with Synthetic Data". In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10446788.
Texto completo da fonteBai, Haoyue, Fengwei Zhou, Lanqing Hong, Nanyang Ye, S. H. Gary Chan e Zhenguo Li. "NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization". In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00821.
Texto completo da fonteSun, Yihong, Adam Kortylewski e Alan Yuille. "Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model". In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.00128.
Texto completo da fonte