We show our models as a sequence to sequence character to word encoder-decoder model. We compare four deep learning models for microtext normalization task which further improve the accuracy of the ...

The pre-trained model is then fine-tuned on macroeconomic data of the target country. In the approach, LSTM-based encoder-decoder aims at learning vector representations of the input data. The ...

RNN Model Details ¦ Recurrent Neural Networks ¦ Deep Learning Posted: 7 May 2025 | Last updated: 7 May 2025 Welcome to Learn with Jay – your go-to channel for mastering new skills and boosting ...

Trias is an encoder-decoder language model trained to reverse-translate protein sequences into codon sequences. It learns codon usage patterns from 10 million mRNA coding sequences across 640 ...

TC4400 is a LDPC decoder core that is fully compliant with ITU G.hn (wireline home networking) specifications. It support a decoded throughput up to 1 ...

For training unsupervised NCE all 6499 images from train set have been used. For Segmentation, contours were created as in .csv files. The images, contours and masks can be found in Segmented Dataset ...

To address the aforementioned problem, we propose a global learning network with large receptive fields (GLNet) based on an encoder-decoder model with skip connections as the basic framework.