├── .obsidian ├── app.json ├── appearance.json ├── core-plugins-migration.json ├── core-plugins.json ├── hotkeys.json └── workspace.json ├── Causal Learning ├── Causal Discovery │ ├── A Survey on Causal Inference-2021.pdf │ └── Papers.md └── Causal Effect Estmation │ ├── Papers.md │ ├── （1983）The central role of the propensity score in observational studies for causal effects.pdf │ ├── （2008IPW加权回归)Weighting regressions by proprnsity scores.pdf │ ├── （2008截断重要性采样)Truncated importance sampling.pdf │ ├── （2009 IPM)IntegralProbabilityMeasures.pdf │ ├── （2010有限样本重要性加权导致高方差)NIPS-2010-learning-bounds-for-importance-weighting-Paper.pdf │ ├── （2011倾向得分-逆概率加权)An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies.pdf │ ├── （2015最小化分布距离度量来学习权重）Stable weights that balance covariates for estimation with incomplete outcome data.pdf │ ├── （2016领域自适应加表示学习）Learning representations for counterfactual inference.pdf │ ├── （2017CVATE）NIPS-2017-causal-effect-inference-with-deep-latent-variable-models-Paper.pdf │ ├── （2017IPW dropout）Deep Counterfactual Networks with Propensity-Dropout (code).pdf │ ├── （2017况琨两变量分解）Treatment effect estimation with data-driven variable decompositionAAAI17-ATE_DVD.pdf │ ├── （2017领域自适应加表示学习）Estimating individual treatment effect-generalization bounds and algorithms(code).pdf │ ├── （2018GANITE）GANITE_estimation_of_individualized_treatment_effects_using_generative_adversarial_nets（code）.pdf │ ├── （2018样本加权）Learning weighted representations for generalization across designs.pdf │ ├── （2018样本重要性加权）CounterFactual Regression with Importance Sampling Weights.pdf │ ├── （2019混杂选择原则)Principles of confounder selection.pdf │ ├── （2019郭若诚)counterfactual evaluation of treatment assignment functions with networked observational data.pdf │ ├── （2019郭若诚）Learning Individual Treatment Effects from Networked Observational Data.pdf │ ├── （2020 综述）A Survey of Learning Causality with Data Problems and Methods.pdf │ ├── （2020况琨三变量分解）Learning Decomposed Representation for Counterfactual.pdf │ ├── （2020分解）Learning disentangled representations for counterfactual regression.pdf │ ├── （2021 张含望）Counterfactual VQA-A Cause-Effect Look at Language Bias.pdf │ ├── （2021 稳性学习)Deep Stable Learning for Out-Of-Distribution Generalization.pdf │ ├── （2021 综述）2021-ACM KDD-A Survey on Causal Inference.pdf │ └── （2021TEDVAE）Treatment Effect Estimation with Disentangled Latent Factors.pdf ├── Causal Representation Learning ├── Out-of-Distribution Generalization │ ├── A Theoretical Analysis on Independence-driven Importance Weighting for.pdf │ ├── Towards Domain Generalization in Object Detection.pdf │ ├── Towards Out-Of-Distribution Generalization.pdf │ └── Uncertainty Modeling for Out-of-Distribution Generalization.pdf ├── Stable Learning │ ├── Causally Regularized Learning with Agnostic Data Selection Bias.pdf │ ├── Counterfactual Prediction for Bundle Treatment.pdf │ ├── Deep Stable Learning for Out-Of-Distribution Generalization.pdf │ ├── Heterogeneous Risk Minimization.pdf │ ├── Stable Learning and its Causal Implication.pdf │ ├── Stable Prediction across Unknown Environments Proceedings of the 24th ACM.pdf │ ├── Stable learning establishes some common ground between causal inference and.pdf │ └── 因果启发的稳定学习年度研究进展2021.11.05.pdf └── test.md ├── README.md └── Uncertainty Estimation ├── Bayesian └── papers.md ├── Ensemble └── papers.md └── Evidential Deep Learning └── papers.md /.obsidian/app.json: -------------------------------------------------------------------------------- 1 | {} -------------------------------------------------------------------------------- /.obsidian/appearance.json: -------------------------------------------------------------------------------- 1 | { 2 | "accentColor": "", 3 | "theme": "system" 4 | } -------------------------------------------------------------------------------- /.obsidian/core-plugins-migration.json: 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Counterfactual Networks with Propensity Dropout 11 | - Treatment effect estimation with data-driven variable decomposition 12 | - GANITE_estimation_of_individualized_treatment_effects_using_generative_adversarial_nets 13 | - Learning weighted representations for generalization across designs 14 | - CounterFactual Regression with Importance Sampling Weights 15 | - counterfactual evaluation of treatment assignment functions with networked observational data 16 | - Learning Decomposed Representation for Counterfactual -------------------------------------------------------------------------------- /Causal Learning/Causal Effect Estmation/（1983）The central role of the propensity score in observational studies for causal effects.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ScorpioBao/Causal-Learning-and-Uncertainty-Estimation/e003871040365bd81b7c51688902cb3170d23b95/Causal Learning/Causal Effect Estmation/（1983）The 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[综述](#1综述) 9 | - [分布外泛化（Out-of-Distribution Generalization）](#2分布外泛化) 10 | - [稳定学习（Stable Learning）](#3稳定学习) 11 | - [消除偏差（Debias）](#4消除偏差) 12 | - [不确定性估计](#3不确定性估计) 13 | - [综述](#1综述) 14 | - [贝叶斯方法（Bayesian）](#2贝叶斯方法) 15 | - [集成方法（Ensemble）](#3集成方法) 16 | - [证据深度学习（Evidential Deep Learning)](#4证据深度学习) 17 | - [ 不确定性估计在不同领域的应用](#4不确定性估计在不同领域的应用) 18 | - [分割](#1分割) 19 | - [目标检测](#2目标检测) 20 | - [开集识别](#3开集识别) 21 | - [分布外泛化](#4分布外泛化) 22 | - [多视图学习](#5多视图学习) 23 | - [深度学习模型校准的相关工作](#5深度学习模型校准的相关工作) 24 | - [代码和数据集](#二代码和数据集) 25 | - [博客](#三博客) 26 | - [因果推理](#1因果推理) 27 | - [不确定性估计](#2不确定性估计) 28 | - [交流](#四交流) 29 | # 一、论文 30 | ### 1、 因果推理(相关论文可以通过目录中的链接访问) 31 | - ##### 计算机视觉中的应用- [MaheepChaudhary/Causation-inComputerVision：该存储库包含有关因果关系的论文列表，以及如何使用相关技术来进一步增强深度学习时代的计算机视觉解决方案。 (github.com)](https://github.com/MaheepChaudhary/Causation-inComputerVision) 32 | ### 2、 因果表征学习 33 | #### （1）综述 34 | - Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021). Toward causal representation learning. _Proceedings of the IEEE_, _109_(5), 612-634.（因果表征学习综述） 35 | - Lu, C., Wu, Y., Hernández-Lobato, J. M., & Schölkopf, B. (2021). Invariant causal representation learning for out-of-distribution generalization. In _International Conference on Learning Representations_.（不变因果表征学习） 36 | 37 | 👆 [BACK to Table of Contents -->](#目录) 38 | #### （2）分布外泛化 39 | *相关论文可以在[分布外泛化](https://github.com/ScorpioBao/Causal-Learning-and-Uncertainty-Estimation/tree/master/Causal%20Representation%20Learning/Out-of-Distribution%20Generalization)文件夹下查看* 40 | - Xu, R., Zhang, X., Shen, Z., Zhang, T., & Cui, P. (2022, June). A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization. In _International Conference on Machine Learning_ (pp. 24803-24829). PMLR.（协变量偏移） 41 | - Liu, C., Sun, X., Wang, J., Tang, H., Li, T., Qin, T., ... & Liu, T. Y. (2021). Learning causal semantic representation for out-of-distribution prediction. _Advances in Neural Information Processing Systems_, _34_, 6155-6170.（因果语义表示学习） 42 | - Zhang, X., Xu, Z., Xu, R., Liu, J., Cui, P., Wan, W., ... & Li, C. (2022). Towards domain generalization in object detection. _arXiv preprint arXiv:2203.14387_.（目标检测中的域泛化） 43 | - Shen, Z., Liu, J., He, Y., Zhang, X., Xu, R., Yu, H., & Cui, P. (2021). Towards out-of-distribution generalization: A survey. _arXiv preprint arXiv:2108.13624_.（综述） 44 | - Li, X., Dai, Y., Ge, Y., Liu, J., Shan, Y., & Duan, L. Y. (2022). Uncertainty modeling for out-of-distribution generalization. _arXiv preprint arXiv:2202.03958_.（OODG不确定性建模） 45 | 46 | 👆 [BACK to Table of Contents -->](#目录) 47 | #### （3）稳定学习 48 | *相关论文可以在[稳定学习](https://github.com/ScorpioBao/Causal-Learning-and-Uncertainty-Estimation/tree/master/Causal%20Representation%20Learning/Stable%20Learning)文件夹下查看* 49 | - Zhang, X., Cui, P., Xu, R., Zhou, L., He, Y., & Shen, Z. (2021). Deep stable learning for out-of-distribution generalization. In _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ (pp. 5372-5382).（StableNet） 50 | - Liu, J., Hu, Z., Cui, P., Li, B., & Shen, Z. (2021, July). Heterogeneous risk minimization. In _International Conference on Machine Learning_ (pp. 6804-6814). PMLR.（异质风险最小化） 51 | - Cui, P., & Athey, S. (2022). Stable learning establishes some common ground between causal inference and machine learning. _Nature Machine Intelligence_, _4_(2), 110-115.（稳定学习与因果推断和机器学习之间的共同点） 52 | 53 | 👆 [BACK to Table of Contents -->](#目录) 54 | #### （4）消除偏差 55 | - Wang, T., Zhou, C., Sun, Q., & Zhang, H. (2021). Causal attention for unbiased visual recognition. In _Proceedings of the IEEE/CVF International Conference on Computer Vision_ (pp. 3091-3100).（因果注意力） 56 | - Niu, Y., Tang, K., Zhang, H., Lu, Z., Hua, X. S., & Wen, J. R. (2021). Counterfactual vqa: A cause-effect look at language bias. In _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ (pp. 12700-12710).（反事实VQA） 57 | - Yang, X., Zhang, H., & Cai, J. (2021). Deconfounded image captioning: A causal retrospect. _IEEE Transactions on Pattern Analysis and Machine Intelligence_.（去除混淆偏差） 58 | - Nam, J., Cha, H., Ahn, S., Lee, J., & Shin, J. (2020). Learning from failure: De-biasing classifier from biased classifier. _Advances in Neural Information Processing Systems_, _33_, 20673-20684.（从有偏分类器学习去偏分类器） 59 | 60 | 👆 [BACK to Table of Contents -->](#目录) 61 | ### 3、不确定性估计 62 | #### （1）综述 63 | - Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., ... & Zhu, X. X. (2021). A survey of uncertainty in deep neural networks. _arXiv preprint arXiv:2107.03342_. 64 | - Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., ... & Nahavandi, S. (2021). A review of uncertainty quantification in deep learning: Techniques, applications and challenges. _Information Fusion_, _76_, 243-297. 65 | - Uncertainty in Deep Learning（Gal博士论文） 66 | - He, W., & Jiang, Z. (2023). A Survey on Uncertainty Quantification Methods for Deep Neural Networks: An Uncertainty Source Perspective. _arXiv preprint arXiv:2302.13425_. 67 | - Hüllermeier, E., & Waegeman, W. (2021). Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. _Machine Learning_, _110_, 457-506.（数据和模型不确定性） 68 | 69 | 👆 [BACK to Table of Contents -->](#目录) 70 | #### （2）贝叶斯方法： 71 | - Gal, Y., & Ghahramani, Z. (2016, June). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In _international conference on machine learning_ (pp. 1050-1059). PMLR.（将Dropout看做贝叶斯近似的经典论文） 72 | - **Kendall, A., & Gal, Y. (2017). What uncertainties do we need in bayesian deep learning for computer vision?. _Advances in neural information processing systems_, _30_.（不确定性估计必读论文，将不确定性分为数据不确定性以及模型不确定性，并介绍了在分类和回归中不确定性估计的建模方法）** 73 | - Louizos, C., & Welling, M. (2017, July). Multiplicative normalizing flows for variational bayesian neural networks. In _International Conference on Machine Learning_ (pp. 2218-2227). PMLR.（变分贝叶斯神经网络，EDL论文中的对比方法） 74 | 75 | 👆 [BACK to Table of Contents -->](#目录) 76 | #### （3）集成方法 77 | - Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2017). Simple and scalable predictive uncertainty estimation using deep ensembles. _Advances in neural information processing systems_, _30_.（集成方法的开山之作） 78 | 79 | 👆 [BACK to Table of Contents -->](#目录) 80 | #### （4）证据深度学习 81 | - **Sensoy, M., Kaplan, L., & Kandemir, M. (2018). Evidential deep learning to quantify classification uncertainty. _Advances in neural information processing systems_, _31_.（证据分类）** 82 | - **Amini, A., Schwarting, W., Soleimany, A., & Rus, D. (2020). Deep evidential regression. _Advances in Neural Information Processing Systems_, _33_, 14927-14937.（证据回归）** 83 | - Sensoy, M., Kaplan, L., Cerutti, F., & Saleki, M. (2020, April). Uncertainty-aware deep classifiers using generative models. In _Proceedings of the AAAI Conference on Artificial Intelligence_ (Vol. 34, No. 04, pp. 5620-5627).（EDL作者的另一篇论文） 84 | - Malinin, A., & Gales, M. (2018). Predictive uncertainty estimation via prior networks. _Advances in neural information processing systems_, _31_.（使用狄利克雷分布建模不确定性的另一种方法） 85 | - Ulmer, D. (2021). A survey on evidential deep learning for single-pass uncertainty estimation. _arXiv preprint arXiv:2110.03051_.（证据不确定性综述） 86 | - Zhao, X., Ou, Y., Kaplan, L., Chen, F., & Cho, J. H. (2019). Quantifying classification uncertainty using regularized evidential neural networks. _arXiv preprint arXiv:1910.06864_.（证据基础上添加正则化项） 87 | 88 | 👆 [BACK to Table of Contents -->](#目录) 89 | ### 4、不确定性估计在不同领域的应用 90 | #### （1）分割 91 | - Kwon, Y., Won, J. H., Kim, B. J., & Paik, M. C. (2020). Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation. _Computational Statistics & Data Analysis_, _142_, 106816.（贝叶斯不确定性） 92 | - Li, H., Nan, Y., Del Ser, J., & Yang, G. (2022). Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation. _Neural Computing and Applications_, 1-15.（证据不确定性） 93 | - Zou, K., Yuan, X., Shen, X., Chen, Y., Wang, M., Goh, R. S. M., ... & Fu, H. (2023). EvidenceCap: Towards trustworthy medical image segmentation via evidential identity cap. _arXiv preprint arXiv:2301.00349_.（证据不确定性） 94 | - Zhou, X., Yue, X., Xu, Z., Denoeux, T., & Chen, Y. (2021, December). Deep neural networks with prior evidence for bladder cancer staging. In _2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)_ (pp. 1221-1226). IEEE.（证据医学影像分割） 95 | 96 | 👆 [BACK to Table of Contents -->](#目录) 97 | #### （2）目标检测 98 | - Harakeh, A., Smart, M., & Waslander, S. L. (2020, May). Bayesod: A bayesian approach for uncertainty estimation in deep object detectors. In _2020 IEEE International Conference on Robotics and Automation (ICRA)_ (pp. 87-93). IEEE.（目标检测中的贝叶斯不确定性估计） 99 | - Feng, D., Harakeh, A., Waslander, S. L., & Dietmayer, K. (2021). A review and comparative study on probabilistic object detection in autonomous driving. _IEEE Transactions on Intelligent Transportation Systems_, _23_(8), 9961-9980.（自动驾驶中的概率目标检测） 100 | - Hang, Q., Li, Z., Dong, Y., & Yue, X. (2022, November). Uncertainty-Aware Deep Open-Set Object Detection. In _Rough Sets: International Joint Conference, IJCRS 2022, Suzhou, China, November 11–14, 2022, Proceedings_ (pp. 161-175). Cham: Springer Nature Switzerland.（证据目标检测） 101 | - Miller, D. (2021). _Epistemic uncertainty estimation for object detection in open-set conditions_ (Doctoral dissertation, Queensland University of Technology).（开集目标检测） 102 | - Gasperini, S., Haug, J., Mahani, M. A. N., Marcos-Ramiro, A., Navab, N., Busam, B., & Tombari, F. (2021). CertainNet: Sampling-free uncertainty estimation for object detection. _IEEE Robotics and Automation Letters_, _7_(2), 698-705.（基于Centernet的不确定度量） 103 | - Nallapareddy, M. R., Sirohi, K., Drews-Jr, P. L., Burgard, W., Cheng, C. H., & Valada, A. (2023). EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning. _arXiv preprint arXiv:2303.03037_.(证据深度学习目标检测，基于Centernet) 104 | - He, Y., Zhu, C., Wang, J., Savvides, M., & Zhang, X. (2019). Bounding box regression with uncertainty for accurate object detection. In _Proceedings of the ieee/cvf conference on computer vision and pattern recognition_ (pp. 2888-2897).（回归不确定性目标检测） 105 | 106 | 👆 [BACK to Table of Contents -->](#目录) 107 | #### （3）开集识别 108 | - Bao, W., Yu, Q., & Kong, Y. (2021). Evidential deep learning for open set action recognition. In _Proceedings of the IEEE/CVF International Conference on Computer Vision_ (pp. 13349-13358).（开集识别） 109 | - Corbière, C., Lafon, M., Thome, N., Cord, M., & Pérez, P. (2021, September). Beyond First-Order Uncertainty Estimation with Evidential Models for Open-World Recognition. In _ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning_.（正则化项） 110 | - Corbière, C., Lafon, M., Thome, N., Cord, M., & Pérez, P. (2021, September). Beyond First-Order Uncertainty Estimation with Evidential Models for Open-World Recognition. In _ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning_.（证据用于开放世界识别） 111 | - Mundt, M., Pliushch, I., Majumder, S., & Ramesh, V. (2019). Open set recognition through deep neural network uncertainty: Does out-of-distribution detection require generative classifiers?. In _Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops_.（OOD检测） 112 | - Zhou, T., Han, T., & Droguett, E. L. (2022). Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework. _Reliability Engineering & System Safety_, _224_, 108525.（贝叶斯故障诊断） 113 | 114 | 👆 [BACK to Table of Contents -->](#目录) 115 | #### （4）分布外泛化 116 | - Chen, L., Lou, Y., He, J., Bai, T., & Deng, M. (2022, June). Evidential neighborhood contrastive learning for universal domain adaptation. In _Proceedings of the AAAI Conference on Artificial Intelligence_ (Vol. 36, No. 6, pp. 6258-6267).（证据领域对比学习） 117 | - Qiao, F., & Peng, X. (2021). Uncertainty-guided model generalization to unseen domains. In _Proceedings of the IEEE/CVF conference on computer vision and pattern recognition_ (pp. 6790-6800).（不确定性指导的数据增广） 118 | - Zhao, L., Liu, T., Peng, X., & Metaxas, D. (2020). Maximum-entropy adversarial data augmentation for improved generalization and robustness. _Advances in Neural Information Processing Systems_, _33_, 14435-14447.（对抗数据增广-最大熵） 119 | - Li, X., Dai, Y., Ge, Y., Liu, J., Shan, Y., & Duan, L. Y. (2022). Uncertainty modeling for out-of-distribution generalization. _arXiv preprint arXiv:2202.03958_.（OODG的不确定性建模） 120 | 121 | 👆 [BACK to Table of Contents -->](#目录) 122 | #### （5）多视图学习 123 | - Han, Z., Zhang, C., Fu, H., & Zhou, J. T. (2022). Trusted multi-view classification with dynamic evidential fusion. _IEEE transactions on pattern analysis and machine intelligence_.（证据多视图分类） 124 | - Ma, H., Han, Z., Zhang, C., Fu, H., Zhou, J. T., & Hu, Q. (2021). Trustworthy multimodal regression with mixture of normal-inverse gamma distributions. _Advances in Neural Information Processing Systems_, _34_, 6881-6893.（证据多模态回归） 125 | - Geng, Y., Han, Z., Zhang, C., & Hu, Q. (2021, May). Uncertainty-aware multi-view representation learning. In _Proceedings of the AAAI Conference on Artificial Intelligence_ (Vol. 35, No. 9, pp. 7545-7553).（多视图回归-数据不确定性建模） 126 | - Zhang, Q., Wu, H., Zhang, C., Hu, Q., Fu, H., Zhou, J. T., & Peng, X. (2023). Provable Dynamic Fusion for Low-Quality Multimodal Data. _arXiv preprint arXiv:2306.02050_.（基于能量函数的不确定性估计） 127 | 128 | 👆 [BACK to Table of Contents -->](#目录) 129 | ### 5、深度学习模型校准的相关工作 130 | - **Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017, July). On calibration of modern neural networks. In _International conference on machine learning_ (pp. 1321-1330). PMLR.（分类校准）** 131 | - **Kuleshov, V., Fenner, N., & Ermon, S. (2018, July). Accurate uncertainties for deep learning using calibrated regression. In _International conference on machine learning_ (pp. 2796-2804). PMLR.（回归校准）** 132 | - Mukhoti, J., Kulharia, V., Sanyal, A., Golodetz, S., Torr, P., & Dokania, P. (2020). Calibrating deep neural networks using focal loss. _Advances in Neural Information Processing Systems_, _33_, 15288-15299.（Focal Loss 校准分类） 133 | - Krishnan, R., & Tickoo, O. (2020). Improving model calibration with accuracy versus uncertainty optimization. _Advances in Neural Information Processing Systems_, _33_, 18237-18248.（考虑不确定性校准模型） 134 | - Thulasidasan, S., Chennupati, G., Bilmes, J. A., Bhattacharya, T., & Michalak, S. (2019). On mixup training: Improved calibration and predictive uncertainty for deep neural networks. _Advances in Neural Information Processing Systems_, _32_.（mixup提高模型校准性能） 135 | 136 | 👆 [BACK to Table of Contents -->](#目录) 137 | 138 | ## 二、代码和数据集 139 | 相关论文的代码以及数据集可以在[Paper With Code ](https://paperswithcode.com/)搜索获取，如果Paper With Code 中没有收录，可直接在GitHub输入论文关键字搜索相关代码 140 | 141 | 👆 [BACK to Table of Contents -->](#目录) 142 | ## 三、博客 143 | ### 1、因果推理 144 | - [e-CARE: 可解释的因果推理评测 (qq.com)](https://mp.weixin.qq.com/s?__biz=MzIxMjAzNDY5Mg==&mid=2650805534&idx=1&sn=ecf1c78e642f46daa10c1217c3bd320d&chksm=8cb880f5bbcf09e3d060f3b96fb24c42ee60c52a1b10914e3af637e8f07c9c88c5ca6f02d17a#rd) 145 | - [【Valse - 崔鹏】Out-of-Distribution 分布外泛化 - 知乎 (zhihu.com)](https://zhuanlan.zhihu.com/p/419346109) 146 | - [崔鹏团队：万字长文梳理「稳定学习」全景图 - 知乎 (zhihu.com)](https://zhuanlan.zhihu.com/p/535602186) 147 | - [【综述】离群/异常/新类检测？开集识别？分布外检测？一文搞懂其间异同！ - 知乎 (zhihu.com)](https://zhuanlan.zhihu.com/p/426521773) 148 | - [因果表征学习最新综述：连接因果科学和机器学习的桥梁 - 知乎 (zhihu.com)](https://zhuanlan.zhihu.com/p/355009051) 149 | - [因果推断：因果表征学习的CV落地 - 知乎 (zhihu.com)](https://zhuanlan.zhihu.com/p/400043237) 150 | 151 | 👆 [BACK to Table of Contents -->](#目录) 152 | ### 2、不确定性估计 153 | - [(184条消息) What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? 计算机视觉用于贝叶斯深度学习的不确定性_Xieyuanli_Chen的博客-CSDN博客](https://blog.csdn.net/weixin_39779106/article/details/78968982#1%E5%B0%86%E5%BC%82%E6%96%B9%E5%B7%AE%E5%81%B6%E7%84%B6%E4%B8%8D%E7%A1%AE%E5%AE%9A%E6%80%A7%E5%92%8C%E8%AE%A4%E7%9F%A5%E4%B8%8D%E7%A1%AE%E5%AE%9A%E6%80%A7%E7%9B%B8%E7%BB%93%E5%90%88%5D(https://blog.csdn.net/weixin_39779106/article/details/78968982#1%E5%B0%86%E5%BC%82%E6%96%B9%E5%B7%AE%E5%81%B6%E7%84%B6%E4%B8%8D%E7%A1%AE%E5%AE%9A%E6%80%A7%E5%92%8C%E8%AE%A4%E7%9F%A5%E4%B8%8D%E7%A1%AE%E5%AE%9A%E6%80%A7%E7%9B%B8%E7%BB%93%E5%90%88)) 154 | - [Bayesian inference problem, MCMC and variational inference | by Joseph Rocca | Towards Data Science](https://towardsdatascience.com/bayesian-inference-problem-mcmc-and-variational-inference-25a8aa9bce29) 155 | - [Uncertainty Estimation in CV - 知乎 (zhihu.com)](https://zhuanlan.zhihu.com/p/166617220) 156 | - [What my deep model doesn't know... | Yarin Gal - Blog | Oxford Machine Learning](http://www.cs.ox.ac.uk/people/yarin.gal/website/blog_3d801aa532c1ce.html) 157 | - [Uncertainty in Deep Learning. How To Measure? | Towards Data Science](https://towardsdatascience.com/my-deep-learning-model-says-sorry-i-dont-know-the-answer-that-s-absolutely-ok-50ffa562cb0b) 158 | - https://www.bilibili.com/video/BV1RJ411D7QA/ 159 | - [如何创造可信任的机器学习模型？先要理解不确定性 (qq.com)](https://mp.weixin.qq.com/s?__biz=MzA3MzI4MjgzMw==&mid=2650755237&idx=3&sn=55beb3edcef0bb4ded4b56e1379efbda&chksm=871a94dbb06d1dcddc49272f77899561c0da5760f2dc6cfebd3877272a959e01c69105a8bac2#rd) 160 | - [从最大似然到EM算法：一致的理解方式 - 科学空间|Scientific Spaces (kexue.fm)](https://kexue.fm/archives/5239) 161 | - [ICML高引模型校准论文，一个好的工作是怎样的 - 知乎 (zhihu.com)](https://zhuanlan.zhihu.com/p/323959089) 162 | 163 | 👆 [BACK to Table of Contents -->](#目录) 164 | ## 四、交流 165 | - Email: senlinbao@gmail.com 166 | 167 | 168 | 169 | 170 | 171 |
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