Self-Supervised Training with Autoencoders for Visual Anomaly Detection

no code implementations • 23 Jun 2022 • Alexander Bauer, Shinichi Nakajima, Klaus-Robert Müller

This insight makes the reconstruction error a natural choice for defining the anomaly score of a sample according to its distance from a corresponding projection on the data manifold.

Anomaly Detection Dimensionality Reduction +2

Polynomial-Time Exact MAP Inference on Discrete Models with Global Dependencies

no code implementations • 27 Dec 2019 • Alexander Bauer, Shinichi Nakajima

Considering the worst-case scenario, junction tree algorithm remains the most general solution for exact MAP inference with polynomial run-time guarantees.

Structured Prediction

Towards Best Practice in Explaining Neural Network Decisions with LRP

1 code implementation • 22 Oct 2019 • Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin

In this paper, we focus on a popular and widely used method of XAI, the Layer-wise Relevance Propagation (LRP).

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +3

Optimizing for Measure of Performance in Max-Margin Parsing

no code implementations • 5 Sep 2017 • Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller

Many statistical learning problems in the area of natural language processing including sequence tagging, sequence segmentation and syntactic parsing has been successfully approached by means of structured prediction methods.

Constituency Parsing Structured Prediction

Minimizing Trust Leaks for Robust Sybil Detection

no code implementations • ICML 2017 • János Höner, Shinichi Nakajima, Alexander Bauer, Klaus-Robert Müller, Nico Görnitz

Sybil detection is a crucial task to protect online social networks (OSNs) against intruders who try to manipulate automatic services provided by OSNs to their customers.