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Found 8 papers, 2 papers with code
Un modèle Transformer Génératif Pré-entrainé pour le _ _ _ _ _ _ français (Generative Pre-trained Transformer in _ _ _ _ _ _ (French) We introduce a French adaptation from the well-known GPT model)
Ces architectures sont en particulier pré-entraînées sur des tâches auto-supervisées et sont ainsi spécifiques pour une langue donnée.
Unifying Parsing and Tree-Structured Models for Generating Sentence Semantic Representations
As a result, the sentence embedding is computed according to an interpretable linguistic pattern and may be used on any downstream task.
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
The choice of a graph learning (GL) model (i. e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks.
Forward Learning of Graph Neural Networks
To address these limitations, the forward-forward algorithm (FF) was recently proposed as an alternative to BP in the image classification domain, which trains NNs by performing two forward passes over positive and negative data.
How Many Layers and Why? An Analysis of the Model Depth in Transformers
In this study, we investigate the role of the multiple layers in deep transformer models.
Contrasting distinct structured views to learn sentence embeddings
We assume structure is crucial to build consistent representations as we expect sentence meaning to be a function from both syntax and semantic aspects.
Sentence embedding with contrastive multi-views learning
We aim to take advantage of this linguist diversity and learn to represent sentences by contrasting these diverse views.
An innovative solution for breast cancer textual big data analysis
With no need for biomedical annotators or pre-defined ontologies, this language-agnostic method reached an good extraction accuracy for several concepts of interest, according to a comparison with a manually structured file, without requiring any existing corpus with local or new notations.
