暂无分享,去创建一个
Xiao Wang | Cheng Yang | Chuan Shi | Hongrui Liu | Xiao Wang | Chuan Shi | Cheng Yang | Hongrui Liu
[1] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[2] Shu Hu,et al. Uncertainty Aware Semi-Supervised Learning on Graph Data , 2020, NeurIPS.
[3] Rich Caruana,et al. Predicting good probabilities with supervised learning , 2005, ICML.
[4] Byron Boots,et al. Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks , 2020, NeurIPS.
[5] Kilian Q. Weinberger,et al. Simplifying Graph Convolutional Networks , 2019, ICML.
[6] A. H. Murphy. A New Vector Partition of the Probability Score , 1973 .
[7] J. Leskovec,et al. Design Space for Graph Neural Networks , 2020, NeurIPS.
[8] Yoshua Bengio,et al. Série Scientifique Scientific Series Incorporating Second-order Functional Knowledge for Better Option Pricing Incorporating Second-order Functional Knowledge for Better Option Pricing , 2022 .
[9] Yinhai Wang,et al. Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting , 2018, IEEE Transactions on Intelligent Transportation Systems.
[10] Peter A. Flach,et al. Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration , 2019, NeurIPS.
[11] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[12] Sunita Sarawagi,et al. Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings , 2018, ICML.
[13] Milos Hauskrecht,et al. Obtaining Well Calibrated Probabilities Using Bayesian Binning , 2015, AAAI.
[14] Xiao Wang,et al. Beyond Low-frequency Information in Graph Convolutional Networks , 2021, AAAI.
[15] Jure Leskovec,et al. How Powerful are Graph Neural Networks? , 2018, ICLR.
[16] Yichen Wei,et al. Learning Region Features for Object Detection , 2018, ECCV.
[17] Zhanxing Zhu,et al. Multi-Stage Self-Supervised Learning for Graph Convolutional Networks , 2020, AAAI.
[18] Huazhu Fu,et al. Trusted Multi-View Classification , 2021, ICLR.
[19] Bruno Ribeiro,et al. Are Graph Neural Networks Miscalibrated? , 2019, ArXiv.
[20] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[21] G. Brier. VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .
[22] Tomas Pfister,et al. Distance-Based Learning from Errors for Confidence Calibration , 2020, ICLR.
[23] Stephan Günnemann,et al. Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking , 2017, ICLR.
[24] Stephan Günnemann,et al. Predict then Propagate: Graph Neural Networks meet Personalized PageRank , 2018, ICLR.
[25] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[26] Hongzhi Yin,et al. Disease Prediction via Graph Neural Networks , 2020, IEEE Journal of Biomedical and Health Informatics.
[27] Bhavya Kailkhura,et al. Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning , 2020, ICML.
[28] Kilian Q. Weinberger,et al. On Calibration of Modern Neural Networks , 2017, ICML.
[29] D. W. Scott,et al. Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .
[30] L. Akoglu,et al. Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs , 2020, NeurIPS.
[31] Lise Getoor,et al. Collective Classification in Network Data , 2008, AI Mag..
[32] Yixin Chen,et al. Link Prediction Based on Graph Neural Networks , 2018, NeurIPS.
[33] John Schulman,et al. Concrete Problems in AI Safety , 2016, ArXiv.
[34] J. Leskovec,et al. Open Graph Benchmark: Datasets for Machine Learning on Graphs , 2020, NeurIPS.
[35] Davide Bacciu,et al. A Fair Comparison of Graph Neural Networks for Graph Classification , 2020, ICLR.
[36] Xiao Wang,et al. AM-GCN: Adaptive Multi-channel Graph Convolutional Networks , 2020, KDD.
[37] Joan Bruna,et al. Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.
[38] Xiao-Ming Wu,et al. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning , 2018, AAAI.
[39] Philip S. Yu,et al. A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[40] Mubarak Shah,et al. In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning , 2021, ICLR.
[41] Zhiyuan Liu,et al. Graph Neural Networks: A Review of Methods and Applications , 2018, AI Open.