Search Results for author: Matthias Schubert

Found 19 papers, 10 papers with code

TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion

1 code implementation spnlp (ACL) 2022 Guirong Fu, Zhao Meng, Zhen Han, Zifeng Ding, Yunpu Ma, Matthias Schubert, Volker Tresp, Roger Wattenhofer

In this paper, we tackle the temporal knowledge graph completion task by proposing TempCaps, which is a Capsule network-based embedding model for Temporal knowledge graph completion.

Entity Embeddings Temporal Knowledge Graph Completion

Spatial-Aware Deep Reinforcement Learning for the Traveling Officer Problem

no code implementations11 Jan 2024 Niklas Strauß, Matthias Schubert

Furthermore, we propose a novel message-passing module for learning future inter-action correlations in the given environment.

reinforcement-learning Stochastic Optimization

MapFormer: Boosting Change Detection by Using Pre-change Information

1 code implementation ICCV 2023 Maximilian Bernhard, Niklas Strauß, Matthias Schubert

Our approach outperforms existing change detection methods by an absolute 11. 7\% and 18. 4\% in terms of binary change IoU on DynamicEarthNet and HRSCD, respectively.

Change Detection Management

Robust Object Detection in Remote Sensing Imagery with Noisy and Sparse Geo-Annotations (Full Version)

1 code implementation24 Oct 2022 Maximilian Bernhard, Matthias Schubert

In order to create the necessary training annotations for object detectors, imagery can be georeferenced and combined with data from other sources, such as points of interest localized by GPS sensors.

Object object-detection +2

Federated Continual Learning for Text Classification via Selective Inter-client Transfer

1 code implementation12 Oct 2022 Yatin Chaudhary, Pranav Rai, Matthias Schubert, Hinrich Schütze, Pankaj Gupta

The objective of Federated Continual Learning (FCL) is to improve deep learning models over life time at each client by (relevant and efficient) knowledge transfer without sharing data.

Continual Learning Federated Learning +3

V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs

no code implementations22 Jul 2022 Christian M. M. Frey, Matthias Schubert

Semantic Web or Knowledge Graphs (KG) emerged to one of the most important information source for intelligent systems requiring access to structured knowledge.

Common Sense Reasoning Disentanglement +3

Box Supervised Video Segmentation Proposal Network

1 code implementation14 Feb 2022 Tanveer Hannan, Rajat Koner, Jonathan Kobold, Matthias Schubert

Video Object Segmentation (VOS) has been targeted by various fully-supervised and self-supervised approaches.

Image Segmentation Motion Compensation +6

APPTeK: Agent-Based Predicate Prediction in Temporal Knowledge Graphs

no code implementations27 Oct 2021 Christian M. M. Frey, Yunpu Ma, Matthias Schubert

In temporal Knowledge Graphs (tKGs), the temporal dimension is attached to facts in a knowledge base resulting in quadruples between entities such as (Nintendo, released, Super Mario, Sep-13-1985), where the predicate holds within a time interval or at a timestamp.

Knowledge Graphs reinforcement-learning +1

SEA: Graph Shell Attention in Graph Neural Networks

no code implementations20 Oct 2021 Christian M. M. Frey, Yunpu Ma, Matthias Schubert

Intuitively, by increasing the number of experts, the models gain in expressiveness such that a node's representation is solely based on nodes that are located within the receptive field of an expert.

Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering

1 code implementation27 Sep 2020 Julian Busch, Evgeniy Faerman, Matthias Schubert, Thomas Seidl

Consequently, our model benefits from a constant number of parameters and a constant-size memory footprint, allowing it to scale to considerably larger datasets.

Clustering Metric Learning

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

4 code implementations20 Mar 2020 Jakaria Rabbi, Nilanjan Ray, Matthias Schubert, Subir Chowdhury, Dennis Chao

Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance.

Generative Adversarial Network Image Enhancement +6

Unsupervised Anomaly Detection for X-Ray Images

1 code implementation29 Jan 2020 Diana Davletshina, Valentyn Melnychuk, Viet Tran, Hitansh Singla, Max Berrendorf, Evgeniy Faerman, Michael Fromm, Matthias Schubert

Therefore, we adopt state-of-the-art approaches for unsupervised learning to detect anomalies and show how the outputs of these methods can be explained.

Unsupervised Anomaly Detection

MMGAN: Generative Adversarial Networks for Multi-Modal Distributions

no code implementations15 Nov 2019 Teodora Pandeva, Matthias Schubert

Moreover, MMGAN allows for clustering real data according to the learned data manifold in the latent space.

Clustering Image Generation

Semi-Supervised Learning on Graphs Based on Local Label Distributions

no code implementations15 Feb 2018 Evgeniy Faerman, Felix Borutta, Julian Busch, Matthias Schubert

Precisely, we propose a new node embedding which is based on the class labels in the local neighborhood of a node.

Attribute General Classification +1

Semi-supervised Outlier Detection using Generative And Adversary Framework

no code implementations ICLR 2018 Jindong Gu, Matthias Schubert, Volker Tresp

In the adversarial process of training CorGAN, the Generator is supposed to generate outlier samples for negative class, and the Discriminator as an one-class classifier is trained to distinguish data from training datasets (i. e. positive class) and generated data from the Generator (i. e. negative class).

General Classification Multi-class Classification +2

Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of The Ancients 2

no code implementations24 Mar 2016 Anders Drachen, Matthew Yancey, John Maguire, Derrek Chu, Iris Yuhui Wang, Tobias Mahlmann, Matthias Schubert, Diego Klabjan

Results indicate that spatio-temporal behavior of MOBA teams is related to team skill, with professional teams having smaller within-team distances and conducting more zone changes than amateur teams.

Clustering Dota 2 +2

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