Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Skianis, Konstantinos"'
Large language models (LLMs) are increasingly used in medical fields. In mental health support, the early identification of linguistic markers associated with mental health conditions can provide valuable support to mental health professionals, and r
Externí odkaz:
http://arxiv.org/abs/2410.12985
Large language models (LLMs) have recently achieved remarkable success in various reasoning tasks in the field of natural language processing. This success of LLMs has also motivated their use in graph-related tasks. Among others, recent work has exp
Externí odkaz:
http://arxiv.org/abs/2409.17906
Large Language Models (LLMs) are increasingly integrated into various medical fields, including mental health support systems. However, there is a gap in research regarding the effectiveness of LLMs in non-English mental health support applications.
Externí odkaz:
http://arxiv.org/abs/2409.17397
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. Over-smoothing and over-squashing are key challenges when st
Externí odkaz:
http://arxiv.org/abs/2212.02374
Food is essential to human survival. So much so that we have developed different recipes to suit our taste needs. In this work, we propose a novel way of creating new, fine-dining recipes from scratch using Transformers, specifically auto-regressive
Externí odkaz:
http://arxiv.org/abs/2209.12774
Since word embeddings have been the most popular input for many NLP tasks, evaluating their quality is of critical importance. Most research efforts are focusing on English word embeddings. This paper addresses the problem of constructing and evaluat
Externí odkaz:
http://arxiv.org/abs/1904.04032
In several domains, data objects can be decomposed into sets of simpler objects. It is then natural to represent each object as the set of its components or parts. Many conventional machine learning algorithms are unable to process this kind of repre
Externí odkaz:
http://arxiv.org/abs/1904.01962
Autor:
Outsios, Stamatis, Skianis, Konstantinos, Meladianos, Polykarpos, Xypolopoulos, Christos, Vazirgiannis, Michalis
Word embeddings are undoubtedly very useful components in many NLP tasks. In this paper, we present word embeddings and other linguistic resources trained on the largest to date digital Greek language corpus. We also present a live web tool for testi
Externí odkaz:
http://arxiv.org/abs/1810.06694
In text classification, the problem of overfitting arises due to the high dimensionality, making regularization essential. Although classic regularizers provide sparsity, they fail to return highly accurate models. On the contrary, state-of-the-art g
Externí odkaz:
http://arxiv.org/abs/1807.04715
Autor:
Siglidis, Giannis, Nikolentzos, Giannis, Limnios, Stratis, Giatsidis, Christos, Skianis, Konstantinos, Vazirgiannis, Michalis
The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing
Externí odkaz:
http://arxiv.org/abs/1806.02193