129 citations · 494 across the 76 of their papers we have counts for
39 papers · 1 filter
FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models
Yao Zhang, Hewei Gao, Haokun Chen +3
Multimodal Large Language Models (MLLMs) excel in tasks like multimodal reasoning and cross-modal retrieval but face deployment challenges in real-world scenarios due to distribute…
Self-Evolving Multi-Agent Systems via Textual Backpropagation
Xiaowen Ma, Yunpu Ma, Chenyang Lin +6
Leveraging multiple Large Language Models (LLMs) has proven effective for addressing complex, high-dimensional tasks, but current approaches often rely on static, manually engineer…
DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models
Zifeng Ding, Yifeng Li, Yuan He +5
Learning useful representations for continuous-time dynamic graphs (CTDGs) is challenging, due to the concurrent need to span long node interaction histories and grasp nuanced temp…
Explaining Deep Neural Networks for Bearing Fault Detection with Vibration Concepts
Thomas Decker, Michael Lebacher, Volker Tresp
Concept-based explanation methods, such as Concept Activation Vectors, are potent means to quantify how abstract or high-level characteristics of input data influence the predictio…
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning
Haokun Chen, Yao Zhang, Denis Krompass +2
Recently, foundation models have exhibited remarkable advancements in multi-modal learning. These models, equipped with millions (or billions) of parameters, typically require a su…
FedPop: Federated Population-based Hyperparameter Tuning
Haokun Chen, Denis Krompass, Jindong Gu +1
Federated Learning (FL) is a distributed machine learning (ML) paradigm, in which multiple clients collaboratively train ML models without centralizing their local data. Similar to…
A Knowledge Graph Perspective on Supply Chain Resilience
Yushan Liu, Bailan He, Marcel Hildebrandt +7
Global crises and regulatory developments require increased supply chain transparency and resilience. Companies do not only need to react to a dynamic environment but have to act p…
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning
Zifeng Ding, Jingpei Wu, Zongyue Li +2
Temporal knowledge graph completion (TKGC) aims to predict the missing links among the entities in a temporal knwoledge graph (TKG). Most previous TKGC methods only consider predic…
Modeling the evolution of temporal knowledge graphs with uncertainty
Soeren Nolting, Zhen Han, Volker Tresp
Forecasting future events is a fundamental challenge for temporal knowledge graphs (tKG). As in real life predicting a mean function is most of the time not sufficient, but the que…
On Calibration of Graph Neural Networks for Node Classification
Tong Liu, Yushan Liu, Marcel Hildebrandt +3
Graphs can model real-world, complex systems by representing entities and their interactions in terms of nodes and edges. To better exploit the graph structure, graph neural networ…
Biologically Inspired Neural Path Finding
Hang Li, Qadeer Khan, Volker Tresp +1
The human brain can be considered to be a graphical structure comprising of tens of billions of biological neurons connected by synapses. It has the remarkable ability to automatic…
Continuous Temporal Graph Networks for Event-Based Graph Data
Jin Guo, Zhen Han, Zhou Su +3
There has been an increasing interest in modeling continuous-time dynamics of temporal graph data. Previous methods encode time-evolving relational information into a low-dimension…
FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation
Haokun Chen, Ahmed Frikha, Denis Krompass +2
Federated Learning (FL) is a decentralized learning paradigm, in which multiple clients collaboratively train deep learning models without centralizing their local data, and hence…
Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction
Zifeng Ding, Bailan He, Yunpu Ma +2
Few-shot relational learning for static knowledge graphs (KGs) has drawn greater interest in recent years, while few-shot learning for temporal knowledge graphs (TKGs) has hardly b…
A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs
Charles Tapley Hoyt, Max Berrendorf, Mikhail Galkin +2
The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics. Here, we review existing rank-based m…
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…
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…
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…