Seuraa
Bertrand Charpentier
Bertrand Charpentier
Vahvistettu sähköpostiosoite verkkotunnuksessa in.tum.de - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts
B Charpentier, D Zügner, S Günnemann
Advances in Neural Information Processing Systems 33, 2020
502020
Hierarchical graph clustering using node pair sampling
T Bonald, B Charpentier, A Galland, A Hollocou
arXiv preprint arXiv:1806.01664, 2018
342018
Scikit-network: Graph Analysis in Python.
T Bonald, N de Lara, Q Lutz, B Charpentier
J. Mach. Learn. Res. 21 (185), 1-6, 2020
282020
Evaluating robustness of predictive uncertainty estimation: Are Dirichlet-based models reliable?
AK Kopetzki, B Charpentier, D Zügner, S Giri, S Günnemann
International Conference on Machine Learning, 5707-5718, 2021
202021
Uncertainty on asynchronous time event prediction
M Biloš, B Charpentier, S Günnemann
Advances in Neural Information Processing Systems 32, 2019
172019
Graph posterior network: Bayesian predictive uncertainty for node classification
M Stadler, B Charpentier, S Geisler, D Zügner, S Günnemann
Advances in Neural Information Processing Systems 34, 18033-18048, 2021
122021
Natural posterior network: Deep bayesian predictive uncertainty for exponential family distributions
B Charpentier, O Borchert, D Zügner, S Geisler, S Günnemann
International Conference on Learning Representations, 2021
112021
On out-of-distribution detection with energy-based models
S Elflein, B Charpentier, D Zügner, S Günnemann
Uncertainty and Robustness in Deep Learning - ICML Workshop, 2021
42021
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning
B Charpentier, R Senanayake, M Kochenderfer, S Günnemann
Distribution-Free Uncertainty Quantification Workshop (DFUQ - ICML), 2022
32022
Differentiable DAG Sampling
B Charpentier, S Kibler, S Günnemann
International Conference on Learning Representations, 2022
32022
Multi-scale clustering in graphs using modularity
B Charpentier
32019
Tree sampling divergence: an information-theoretic metric for hierarchical graph clustering
B Charpentier, T Bonald
IJCAI-19, 2019
22019
Winning the Lottery Ahead of Time: Efficient Early Network Pruning
J Rachwan, D Zügner, B Charpentier, S Geisler, M Ayle, S Günnemann
International Conference on Machine Learning, 18293-18309, 2022
12022
Learning Graph Representations by Dendrograms
T Bonald, B Charpentier
arXiv preprint arXiv:1807.05087, 2018
12018
On the Robustness and Anomaly Detection of Sparse Neural Networks
M Ayle, B Charpentier, J Rachwan, D Zügner, S Geisler, S Günnemann
Sparsity in Neural Networks Workshop (SNN), 2022
2022
End-to-End Learning of Probabilistic Hierarchies on Graphs
D Zügner, B Charpentier, M Ayle, S Geringer, S Günnemann
International Conference on Learning Representations, 2021
2021
Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributions
B Charpentier, O Borchert, D Zügner, S Geisler, S Günnemann
arXiv e-prints, arXiv: 2105.04471, 2021
2021
Learning Graph Representations by Dendrograms
B Charpentier, T Bonald
arXiv. org, 2018
2018
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Artikkelit 1–18