2 citations · 3 across the 3 of their papers we have counts for
6 papers
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference
Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig +1
Full Bayesian posteriors are rarely analytically tractable, which is why real-world Bayesian inference heavily relies on approximate techniques. Approximations generally differ fro…
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning
Runa Eschenhagen, Erik Daxberger, Philipp Hennig +1
Deep neural networks are prone to overconfident predictions on outliers. Bayesian neural networks and deep ensembles have both been shown to mitigate this problem to some extent. I…
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
The point estimates of ReLU classification networks---arguably the most widely used neural network architecture---have been shown to yield arbitrarily high confidence far away from…
Predictive Uncertainty Quantification with Compound Density Networks
Agustinus Kristiadi, Sina Däubener, Asja Fischer
Despite the huge success of deep neural networks (NNs), finding good mechanisms for quantifying their prediction uncertainty is still an open problem. Bayesian neural networks are…
Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge
Debanjan Chaudhuri, Agustinus Kristiadi, Jens Lehmann +1
Building systems that can communicate with humans is a core problem in Artificial Intelligence. This work proposes a novel neural network architecture for response selection in an…
Incorporating Literals into Knowledge Graph Embeddings
Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov +2
Knowledge graphs, on top of entities and their relationships, contain other important elements: literals. Literals encode interesting properties (e.g. the height) of entities that…