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Found 8 papers, 5 papers with code
Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
Graph generation has emerged as a crucial task in machine learning, with significant challenges in generating graphs that accurately reflect specific properties.
Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformers
These results highlight the transformative impact of multimodal models, specifically the fusion of GNNs and LLMs, empowering researchers with powerful tools for more accurate function prediction of existing as well as first-to-see proteins.
GreekBART: The First Pretrained Greek Sequence-to-Sequence Model
In addition, we examine its performance on two NLG tasks from GreekSUM, a newly introduced summarization dataset for the Greek language.
Word Sense Induction with Hierarchical Clustering and Mutual Information Maximization
In this paper, we propose a novel unsupervised method based on hierarchical clustering and invariant information clustering (IIC).
Political Communities on Twitter: Case Study of the 2022 French Presidential Election
We perform community detection on a retweet graph of users and propose an in-depth analysis of the stance of each community.
NLP Research and Resources at DaSciM, Ecole Polytechnique
DaSciM (Data Science and Mining) part of LIX at Ecole Polytechnique, established in 2013 and since then producing research results in the area of large scale data analysis via methods of machine and deep learning.
JuriBERT: A Masked-Language Model Adaptation for French Legal Text
Language models have proven to be very useful when adapted to specific domains.
Evaluation Of Word Embeddings From Large-Scale French Web Content
Adding that pretrained word vectors on huge text corpus achieved high performance in many different NLP tasks.
