Volker Tresp

Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods with Adjusted Mean Rank

2020 · arXiv (Cornell University)

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 current experimental setting, multiple different scores …

Improving the Robustness of Capsule Networks to Image Affine Transformations

2020

Convolutional neural networks (CNNs) achieve translational invariance by using pooling operations. However, the operations do not preserve the spatial relationships in the learned representations. Hence, CNNs cannot extrapolate to various geometric transformations of inputs. Recently, …

Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing

2020 · arXiv (Cornell University)

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 dependencies, which can be captured by patterns described as …

TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs

2022 · Proceedings of the AAAI Conference on Artificial Intelligence

Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types. However, information and knowledge evolve continuously, and temporal dynamics emerge, which are expected to influence future situations. …

Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information

2022 · arXiv (Cornell University)

Knowledge graph completion (KGC) aims to predict the missing links among knowledge graph (KG) entities. Though various methods have been developed for KGC, most of them can only deal with the KG entities seen in …

Workshop Summary: Quantum Machine Learning

2023

Quantum computing (QC) has made significant progress in recent years, and scientists are exploring its applications across various fields, including quantum machine learning (QML).

FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning

2024 · Proceedings of the AAAI Conference on Artificial Intelligence

Recently, foundation models have exhibited remarkable advancements in multi-modal learning. These models, equipped with millions (or billions) of parameters, typically require a substantial amount of data for finetuning. However, collecting and centralizing training data from …

Why long model-based rollouts are no reason for bad Q-value estimates

2024

This paper explores the use of model-based offline reinforcement learning with long model rollouts.While some literature criticizes this approach due to compounding errors, many practitioners have found success in real-world applications.The paper aims to demonstrate …