Search Results for author: Matthias Schubert

Found 12 papers, 5 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 Knowledge Graph Completion +1

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.

Motion Compensation Semantic Segmentation +3

MIRA: Multihop Relation Prediction in Temporal Knowledge Graphs

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

Given query entities, our agent starts to gather temporal relevant information about the neighborhood of the subject and object.

Knowledge Graphs

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.

Metric Learning

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

3 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.

Image Enhancement Remote Sensing Image Classification +3

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.

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.

General Classification Node Classification

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 +1

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.

Dota 2 Time Series +1

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