129 citations · 494 across the 76 of their papers we have counts for
21 papers · 1 filter
TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs
Yushan Liu, Yunpu Ma, Marcel Hildebrandt +2
Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types. However, information and knowledge evolve continuous…
A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion
Zifeng Ding, Yunpu Ma, Bailan He +1
Knowledge graphs contain rich knowledge about various entities and the relational information among them, while temporal knowledge graphs (TKGs) describe and model the interactions…
Generating Table Vector Representations
Aneta Koleva, Martin Ringsquandl, Mitchell Joblin +1
High-quality Web tables are rich sources of information that can be used to populate Knowledge Graphs (KG). The focus of this paper is an evaluation of methods for table-to-class a…
Towards Data-Free Domain Generalization
Ahmed Frikha, Haokun Chen, Denis Krompaß +2
In this work, we investigate the unexplored intersection of domain generalization (DG) and data-free learning. In particular, we address the question: How can knowledge contained i…
Description-based Label Attention Classifier for Explainable ICD-9 Classification
Malte Feucht, Zhiliang Wu, Sophia Althammer +1
ICD-9 coding is a relevant clinical billing task, where unstructured texts with information about a patient's diagnosis and treatments are annotated with multiple ICD-9 codes. Auto…
The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding
Volker Tresp, Sahand Sharifzadeh, Hang Li +2
We present a unified computational theory of an agent's perception and memory. In our model, perception, episodic memory, and semantic memory are realized by different operational…
Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption
Ahmed Frikha, Denis Krompaß, Volker Tresp
Machine learning models that can generalize to unseen domains are essential when applied in real-world scenarios involving strong domain shifts. We address the challenging domain g…
Adaptive Multi-Resolution Attention with Linear Complexity
Yao Zhang, Yunpu Ma, Thomas Seidl +1
Transformers have improved the state-of-the-art across numerous tasks in sequence modeling. Besides the quadratic computational and memory complexity w.r.t the sequence length, the…
Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models
Zhiliang Wu, Yinchong Yang, Peter A. Fasching +1
Recurrent neural network based solutions are increasingly being used in the analysis of longitudinal Electronic Health Record data. However, most works focus on prediction accuracy…
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering
Rajat Koner, Hang Li, Marcel Hildebrandt +3
Visual Question Answering (VQA) is concerned with answering free-form questions about an image. Since it requires a deep semantic and linguistic understanding of the question and t…
Scenes and Surroundings: Scene Graph Generation using Relation Transformer
Rajat Koner, Poulami Sinhamahapatra, Volker Tresp
Identifying objects in an image and their mutual relationships as a scene graph leads to a deep understanding of image content. Despite the recent advancement in deep learning, the…
Improving Inductive Link Prediction Using Hyper-Relational Facts
Mehdi Ali, Max Berrendorf, Mikhail Galkin +4
For many years, link prediction on knowledge graphs (KGs) has been a purely transductive task, not allowing for reasoning on unseen entities. Recently, increasing efforts are put i…
OODformer: Out-Of-Distribution Detection Transformer
Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher +2
A serious problem in image classification is that a trained model might perform well for input data that originates from the same distribution as the data available for model train…
Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning
Zhiliang Wu, Yinchong Yang, Jindong Gu +1
Deep neural networks are increasingly being used for the analysis of medical images. However, most works neglect the uncertainty in the model's prediction. We propose an uncertaint…
Capsule Network is Not More Robust than Convolutional Network
Jindong Gu, Volker Tresp, Han Hu
The Capsule Network is widely believed to be more robust than Convolutional Networks. However, there are no comprehensive comparisons between these two networks, and it is also unk…
Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs
Yushan Liu, Marcel Hildebrandt, Mitchell Joblin +3
Biomedical knowledge graphs permit an integrative computational approach to reasoning about biological systems. The nature of biological data leads to a graph structure that differ…
Mutual Information State Intrinsic Control
Rui Zhao, Yang Gao, Pieter Abbeel +2
Reinforcement learning has been shown to be highly successful at many challenging tasks. However, success heavily relies on well-shaped rewards. Intrinsically motivated RL attempts…
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification
Julian Busch, Anton Kocheturov, Volker Tresp +1
Malicious software (malware) poses an increasing threat to the security of communication systems as the number of interconnected mobile devices increases exponentially. While some…
Effective and Efficient Vote Attack on Capsule Networks
Jindong Gu, Baoyuan Wu, Volker Tresp
Standard Convolutional Neural Networks (CNNs) can be easily fooled by images with small quasi-imperceptible artificial perturbations. As alternatives to CNNs, the recently proposed…
Improving Scene Graph Classification by Exploiting Knowledge from Texts
Sahand Sharifzadeh, Sina Moayed Baharlou, Martin Schmitt +2
Training scene graph classification models requires a large amount of annotated image data. Meanwhile, scene graphs represent relational knowledge that can be modeled with symbolic…
Temporal Knowledge Graph Forecasting with Neural ODE
Zhen Han, Zifeng Ding, Yunpu Ma +2
There has been an increasing interest in inferring future links on temporal knowledge graphs (KG). While links on temporal KGs vary continuously over time, the existing approaches…