Agustinus Kristiadi

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Philipp HennigUniversity of Tübingen & Max Planck Institute for Intelligent SystemsVerified email at tue.mpg.de
Matthias HeinProfessor of Computer Science, University of TübingenVerified email at uni-tuebingen.de
Asja FischerProfessor for Machine Learning, Ruhr University BochumVerified email at ini.rub.de
Jens LehmannProfessor of Computer Science at the University of Bonn & Fraunhofer IAISVerified email at cs.uni-bonn.de
Runa EschenhagenMSc Student, University of TübingenVerified email at student.uni-tuebingen.de
Erik DaxbergerPhD at University of Cambridge & MPI for Intelligent SystemsVerified email at cam.ac.uk
Matthias BauerDeepMind, LondonVerified email at google.com
Alexander ImmerPhD student, ETH Zürich, Max Planck Institute for Intelligent SystemsVerified email at inf.ethz.ch
Luca RendsburgUniversity of TübingenVerified email at uni-tuebingen.de
Ulrike von LuxburgUniversity of TübingenVerified email at informatik.uni-tuebingen.de

Agustinus Kristiadi

University of Tübingen

Verified email at uni-tuebingen.de - Homepage

Bayesian Deep Learning Riemannian Geometry Information Geometry


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks A Kristiadi, M Hein, P Hennig ICML 2020, 2020	84	2020
Incorporating literals into knowledge graph embeddings A Kristiadi, MA Khan, D Lukovnikov, J Lehmann, A Fischer ISWC 2019, 2019	61	2019
Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge D Chaudhuri, A Kristiadi, J Lehmann, A Fischer CoNLL 2018, 2018	22	2018
Laplace Redux--Effortless Bayesian Deep Learning E Daxberger, A Kristiadi, A Immer, R Eschenhagen, M Bauer, ... NeurIPS 2021, 2021	20	2021
Predictive uncertainty quantification with compound density networks A Kristiadi, S Däubener, A Fischer Bayesian Deep Learning Workshop, NeurIPS 2019, 2019	10	2019
Deep Convolutional Level Set Method for Image Segmentation. A Kristiadi Journal of ICT Research & Applications 11 (3), 2017	10	2017
Fast predictive uncertainty for classification with Bayesian deep networks M Hobbhahn, A Kristiadi, P Hennig UAI 2022, 2022	9	2022
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence A Kristiadi, M Hein, P Hennig NeurIPS 2021, 2021	6*	2021
Learnable Uncertainty under Laplace Approximations A Kristiadi, M Hein, P Hennig UAI 2021, 2021	6	2021
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning R Eschenhagen, E Daxberger, P Hennig, A Kristiadi Bayesian Deep Learning Workshop, NeurIPS 2021, 2021	5	2021
Parallel particle swarm optimization for image segmentation A Kristiadi, P Mudjihartono Universitas Atma Jaya Yogyakarta, 2013	4	2013
Being a Bit Frequentist Improves Bayesian Neural Networks A Kristiadi, M Hein, P Hennig AISTATS 2022, 2022	1	2022
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks A Kristiadi, R Eschenhagen, P Hennig arXiv preprint arXiv:2205.10041, 2022		2022
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference L Rendsburg, A Kristiadi, P Hennig, U von Luxburg AISTATS 2022, 2022		2022

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