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The interdependence between nodes in graphs is key to improve class predictions on nodes and utilized in approaches like Label Propagation (LP) or in Graph Neural Networks (GNN).
Bayesian graph convolutional neural networks using non-parametric graph learning
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M. P. Naeini, G. Cooper, and M. Hauskrecht · 2015
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D. Rezende and S. Mohamed · 2015
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Y. Gal · 2016
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Semi-Supervised Classification with Graph Convolutional Networks
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
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Adversarial examples are not easily detected: Bypassing ten detection methods
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The power of certainty: A dirichlet-multinomial model for belief propagation
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C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
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J. Hu, R. Cheng, Z. Huang, Y. Fang, and S. Luo · 2017
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Incorporating uncertainty into deep learning for spoken language assessment
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P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2017
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Bayesgrad: Explaining predictions of graph convolutional networks
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Understanding deep neural networks with rectified linear units
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Bayesian robust attributed graph clustering: Joint learning of partial anomalies and group structure
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Fastgcn: fast learning with graph convolutional networks via importance sampling
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Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning
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Y. Gal and L. Smith · 2018
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Variational bayesian dropout: pitfalls and fixes
J. Hron, A. Matthews, and Z. Ghahramani · 2018
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A. Malinin and M. Gales · 2018
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M. W. Dusenberry, G. Jerfel, Y. Wen, Y.-A. Ma, J. Snoek, K. Heller, B. Lakshminarayanan, and D. Tran · 2020
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Variational inference for graph convolutional networks in the absence of graph data and adversarial settings
P. Elinas, E. V. Bonilla, and L. Tiao · 2020
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Liberty or depth: Deep bayesian neural nets do not need complex weight posterior approximations
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Uncertainty-aware attention graph neural network for defending adversarial attacks
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Bayesian graph neural networks with adaptive connection sampling
A. Hasanzadeh, E. Hajiramezanali, S. Boluki, M. Zhou, N. Duffield, K. Narayanan, and X. Qian · 2020
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Bayesian semi-supervised learning with graph gaussian processes
Y. C. Ng, N. Colombo, and R. Silva · 2018
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Evidential deep learning to quantify classification uncertainty
M. Sensoy, L. Kaplan, and M. Kandemir · 2018
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Pitfalls of graph neural network evaluation
O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann · 2018
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Hiding individuals and communities in a social network
M. Waniek, T. P. Michalak, M. J. Wooldridge, and T. Rahwan · 2018
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Uncertainty-based continual learning with adaptive regularization
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Generative ensembles for robust anomaly detection
H. Choi, E. Jang, and A. A. Alemi · 2019
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Uncertainty quantification using neural networks for molecular property prediction, 2020
L. Hirschfeld, K. Swanson, K. Yang, R. Barzilay, and C. W. Coley · 2020
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Open graph benchmark: Datasets for machine learning on graphs
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Combining label propagation and simple models out-performs graph neural networks
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Why normalizing flows fail to detect out-of-distribution data
P. Kirichenko, P. Izmailov, and A. G. Wilson · 2020
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Evaluating robustness of predictive uncertainty estimation: Are dirichlet-based models reliable?
A. Kopetzki, B. Charpentier, D. Zügner, S. Giri, and S. Günnemann · 2020
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Being bayesian, even just a bit, fixes overconfidence in relu networks, 2020
A. Kristiadi, M. Hein, and P. Hennig · 2020
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
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Perfect density models cannot guarantee anomaly detection
C. L. Lan and L. Dinh · 2020
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Learning to balance: Bayesian meta-learning for imbalanced and out-of-distribution tasks, 2020
H. B. Lee, H. Lee, D. Na, S. Kim, M. Park, E. Yang, and S. J. Hwang · 2020
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Towards neural networks that provably know when they don’t know
A. Meinke and M. Hein · 2020
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Interpretable machine learning, 2020
C. Molnar · 2020
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Density of states estimation for out-of-distribution detection
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Detecting out-of-distribution inputs to deep generative models using typicality
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Confidence-calibrated adversarial training: Generalizing to unseen attacks
D. Stutz, M. Hein, and B. Schiele · 2020
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Methods for comparing uncertainty quantifications for material property predictions, 2020
K. Tran, W. Neiswanger, J. Yoon, Q. Zhang, E. Xing, and Z. W. Ulissi · 2020
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Uncertainty estimation using a single deep deterministic neural network, 2020
J. van Amersfoort, L. Smith, Y. W. Teh, and Y. Gal · 2020
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Unifying graph convolutional neural networks and label propagation
H. Wang and J. Leskovec · 2020
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Microsoft academic graph: When experts are not enough
K. Wang, Z. Shen, C. Huang, C.-H. Wu, Y. Dong, and A. Kanakia · 2020
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Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Y. Wen, D. Tran, and J. Ba · 2020
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Hyperparameter ensembles for robustness and uncertainty quantification
F. Wenzel, J. Snoek, D. Tran, and R. Jenatton · 2020
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Contrastive training for improved out-of-distribution detection
J. Winkens, R. Bunel, A. Guha Roy, R. Stanforth, V. Natarajan, J. R. Ledsam, P. MacWilliams, P. Kohli, A. Karthikesalingam, S. Kohl, T. Cemgil, S. M. A. Eslami, and O. Ronneberger · 2020
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I-gcn: Robust graph convolutional network via influence mechanism
H. Zhan and X. Pei · 2020
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Uncertainty aware semi-supervised learning on graph data
X. Zhao, F. Chen, S. Hu, and J.-H. Cho · 2020
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Gaussian processes on graphs via spectral kernel learning
Y.-C. Zhi, Y. C. Ng, and X. Dong · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, and D. Koutra · 2020
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Matérn Gaussian Processes on Graphs
V. Borovitskiy, I. Azangulov, A. Terenin, P. Mostowsky, M. Deisenroth, and N. Durrande · 2021
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