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Found 13 papers, 6 papers with code
Evaluation of Machine Translation Methods applied to Medical Terminologies
Medical terminologies resources and standards play vital roles in clinical data exchanges, enabling significantly the services’ interoperability within healthcare national information networks.
On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks
Graph Neural Networks (GNNs) have succeeded in various computer science applications, yet deep GNNs underperform their shallow counterparts despite deep learning's success in other domains.
Towards Fine-Dining Recipe Generation with Generative Pre-trained Transformers
Food is essential to human survival.
Evaluation of Greek Word Embeddings
Since word embeddings have been the most popular input for many NLP tasks, evaluating their quality is of critical importance.
Rep the Set: Neural Networks for Learning Set Representations
In several domains, data objects can be decomposed into sets of simpler objects.
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Word Embeddings from Large-Scale Greek Web Content
Word embeddings are undoubtedly very useful components in many NLP tasks.
Orthogonal Matching Pursuit for Text Classification
In text classification, the problem of overfitting arises due to the high dimensionality, making regularization essential.
GraKeL: A Graph Kernel Library in Python
The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines.
Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text Classification
Contrary to the traditional Bag-of-Words approach, we consider the Graph-of-Words(GoW) model in which each document is represented by a graph that encodes relationships between the different terms.
Kernel Graph Convolutional Neural Nets
Graph kernels have been successfully applied to many graph classification problems.
Kernel Graph Convolutional Neural Networks
Graph kernels have been successfully applied to many graph classification problems.
