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
Runa EschenhagenPhD student, University of CambridgeVerified email at cam.ac.uk
Jens LehmannPrincipal Scientist at Amazon Alexa AI, Honorary Professor at TU DresdenVerified email at amazon.com
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
Felix DangelPostdoc at the Vector Institute, TorontoVerified email at vectorinstitute.ai

Agustinus Kristiadi

Postdoc at the Vector Institute

Verified email at vectorinstitute.ai - 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	153	2020
Laplace Redux--Effortless Bayesian Deep Learning E Daxberger, A Kristiadi, A Immer, R Eschenhagen, M Bauer, ... NeurIPS 2021, 2021	95	2021
Incorporating literals into knowledge graph embeddings A Kristiadi, MA Khan, D Lukovnikov, J Lehmann, A Fischer ISWC 2019, 2019	81	2019
Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge D Chaudhuri, A Kristiadi, J Lehmann, A Fischer CoNLL 2018, 2018	26	2018
Learnable Uncertainty under Laplace Approximations A Kristiadi, M Hein, P Hennig UAI 2021, 2021	15	2021
Deep Convolutional Level Set Method for Image Segmentation. A Kristiadi Journal of ICT Research & Applications 11 (3), 2017	13	2017
Fast predictive uncertainty for classification with Bayesian deep networks M Hobbhahn, A Kristiadi, P Hennig UAI 2022, 2022	12	2022
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence A Kristiadi, M Hein, P Hennig NeurIPS 2021, 2021	10*	2021
Predictive uncertainty quantification with compound density networks A Kristiadi, S Däubener, A Fischer Bayesian Deep Learning Workshop, NeurIPS 2019, 2019	9*	2019
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	8	2021
Being a Bit Frequentist Improves Bayesian Neural Networks A Kristiadi, M Hein, P Hennig AISTATS 2022, 2022	4	2022
Parallel particle swarm optimization for image segmentation A Kristiadi, P Mudjihartono Universitas Atma Jaya Yogyakarta, 2013	4	2013
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	2	2022
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization A Kristiadi, F Dangel, P Hennig arXiv preprint arXiv:2302.07384, 2023		2023
Low-Cost Bayesian Methods for Fixing Neural Networks' Overconfidence A Kristiadi Universität Tübingen, 2023		2023
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks A Kristiadi, R Eschenhagen, P Hennig NeurIPS 2022, 2022		2022

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