Actualités

LinkedIn5 mois
Meilleure architecture d’encodeur-décodeur pour les modèles Seq2Seq - LinkedIn
Les modèles Seq2seq sont des outils puissants pour le traitement du langage naturel, la traduction automatique, la reconnaissance vocale, etc. Ils utilisent une architecture codeur-décodeur, où ...
GitHub4 a
GitHub - syedshahzadraza/Encoder-Decoder-Model-with-Attention: We train a sequence to sequence (seq2seq) model for neural machine translation from French to English using ...
The objective of this project is to train a seq2seq model (an encoder-decoder model with attention) for machine translation. This project is motivated by the idea of implementing an advanced ...
GitHub3 a
GitHub - znawfar/NMT-with-Attention: I have implemented encoder-decoder based seq2seq models with attention. The first aim for this implementation is to work towards ...
I have implemented encoder-decoder based seq2seq models with attention. The first aim for this implementation is to work towards computational grounded theory and thematic analysis using NLP and deep ...
GitHub24 a
tensorflow/nmt: TensorFlow Neural Machine Translation Tutorial - GitHub
Remember that in the vanilla seq2seq model, we pass the last source state from the encoder to the decoder when starting the decoding process. This works well for short and medium-length sentences; ...
Frontiers4 a
Clustering and Recognition of Spatiotemporal Features Through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks - Frontiers
1 Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, United States; 2 Department of Applied Mathematics, University of Washington, Seattle, WA, United States; ...