Fast Predictive Uncertainty for Classification with Bayesian Deep Networks
Marius Hobbhahn, Agustinus Kristiadi, and Philipp Hennig.
UAI 2022
[
arxiv]
Being a Bit Frequentist Improves Bayesian Neural Networks
Agustinus Kristiadi, Matthias Hein, and Philipp Hennig.
AISTATS 2022
[
arxiv]
[
code]
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference
Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, and Ulrike von Luxburg.
AISTATS 2022
[
arxiv]
[
code]
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence
Agustinus Kristiadi, Matthias Hein, and Philipp Hennig.
Laplace Redux -- Effortless Bayesian Deep Learning
Erik Daxberger*, Agustinus Kristiadi*, Alexander Immer*, Runa Eschenhagen*, Matthias Bauer, and Philipp Hennig.
Learnable Uncertainty under Laplace Approximations
Agustinus Kristiadi, Matthias Hein, and Philipp Hennig.
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
Agustinus Kristiadi, Matthias Hein, and Philipp Hennig.
Incorporating Literals into Knowledge Graph Embeddings
Agustinus Kristiadi*, Mohammad Asif Khan*, Denis Lukovnikov, Jens Lehmann, and Asja Fischer.
Improving Response Selection in Multi-turn Dialogue Systems by Incorporating Domain Knowledge
Debanjan Chauduri, Agustinus Kristiadi, Jens Lehmann, Asja Fischer.
Deep Convolutional Level Set Method for Image Segmentation
Agustinus Kristiadi and Pranowo.
Journal of ICT Research and Applications 11.3 (2017)
[
pdf]
[
code]
Parallel Particle Swarm Optimization for Image Segmentation
Agustinus Kristiadi, Pranowo, and Paulus Mudjihartono.