129 citations · 494 across the 76 of their papers we have counts for
13 papers · 1 filter
Classification by Attention: Scene Graph Classification with Prior Knowledge
Sahand Sharifzadeh, Sina Moayed Baharlou, Volker Tresp
A major challenge in scene graph classification is that the appearance of objects and relations can be significantly different from one image to another. Previous works have addres…
DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion
Zhen Han, Yunpu Ma, Peng Chen +1
There has recently been increasing interest in learning representations of temporal knowledge graphs (KGs), which record the dynamic relationships between entities over time. Tempo…
xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links
Zhen Han, Peng Chen, Yunpu Ma +1
Modeling time-evolving knowledge graphs (KGs) has recently gained increasing interest. Here, graph representation learning has become the dominant paradigm for link prediction on t…
Interpretable Graph Capsule Networks for Object Recognition
Jindong Gu, Volker Tresp
Capsule Networks, as alternatives to Convolutional Neural Networks, have been proposed to recognize objects from images. The current literature demonstrates many advantages of Caps…
Introspective Learning by Distilling Knowledge from Online Self-explanation
Jindong Gu, Zhiliang Wu, Volker Tresp
In recent years, many explanation methods have been proposed to explain individual classifications of deep neural networks. However, how to leverage the created explanations to imp…
ARCADe: A Rapid Continual Anomaly Detector
Ahmed Frikha, Denis Krompaß, Volker Tresp
Although continual learning and anomaly detection have separately been well-studied in previous works, their intersection remains rather unexplored. The present work addresses a le…
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt +4
Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs. While each of them…
Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing
Yushan Liu, Marcel Hildebrandt, Mitchell Joblin +2
The graph structure of biomedical data differs from those in typical knowledge graph benchmark tasks. A particular property of biomedical data is the presence of long-range depende…
Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score
Zhiliang Wu, Yinchong Yang, Yunpu Ma +4
Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups. With the advance of electron…
Scene Graph Reasoning for Visual Question Answering
Marcel Hildebrandt, Hang Li, Rajat Koner +2
Visual question answering is concerned with answering free-form questions about an image. Since it requires a deep linguistic understanding of the question and the ability to assoc…
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework
Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt +6
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult. In order to as…
Causal Inference under Networked Interference and Intervention Policy Enhancement
Yunpu Ma, Volker Tresp
Estimating individual treatment effects from data of randomized experiments is a critical task in causal inference. The Stable Unit Treatment Value Assumption (SUTVA) is usually ma…
On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
Max Berrendorf, Evgeniy Faerman, Laurent Vermue +1
In this work, we take a closer look at the evaluation of two families of methods for enriching information from knowledge graphs: Link Prediction and Entity Alignment. In the curre…