14 citations · 25 across the 8 of their papers we have counts for
8 papers
Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies
Christos Theodoropoulos, Natasha Mulligan, Joao Bettencourt-Silva
Developing novel predictive models with complex biomedical information is challenging due to various idiosyncrasies related to heterogeneity, standardization or sparseness of the d…
Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors
Andrei C. Coman, Christos Theodoropoulos, Marie-Francine Moens +1
We propose Fast-and-Frugal Text-Graph (FnF-TG) Transformers, a Transformer-based framework that unifies textual and structural information for inductive link prediction in text-att…
Enhancing Biomedical Knowledge Discovery for Diseases: An Open-Source Framework Applied on Rett Syndrome and Alzheimer's Disease
Christos Theodoropoulos, Andrei Catalin Coman, James Henderson +1
The ever-growing volume of biomedical publications creates a critical need for efficient knowledge discovery. In this context, we introduce an open-source end-to-end framework desi…
GADePo: Graph-Assisted Declarative Pooling Transformers for Document-Level Relation Extraction
Andrei C. Coman, Christos Theodoropoulos, Marie-Francine Moens +1
Document-level relation extraction typically relies on text-based encoders and hand-coded pooling heuristics to aggregate information learned by the encoder. In this paper, we leve…
Representation Learning for Person or Entity-centric Knowledge Graphs: An Application in Healthcare
Christos Theodoropoulos, Natasha Mulligan, Thaddeus Stappenbeck +1
Knowledge graphs (KGs) are a popular way to organise information based on ontologies or schemas and have been used across a variety of scenarios from search to recommendation. Desp…
An Information Extraction Study: Take In Mind the Tokenization!
Christos Theodoropoulos, Marie-Francine Moens
Current research on the advantages and trade-offs of using characters, instead of tokenized text, as input for deep learning models, has evolved substantially. New token-free model…
Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning
Christos Theodoropoulos, James Henderson, Andrei C. Coman +1
Though language model text embeddings have revolutionized NLP research, their ability to capture high-level semantic information, such as relations between entities in text, is lim…
Automatic artifact removal of resting-state fMRI with Deep Neural Networks
Christos Theodoropoulos, Christos Chatzichristos, Sabine Van Huffel
Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for studying brain activity. During an fMRI session, the subject executes a set of tasks (task-related fMRI…