The ability of transformers to handle data sequences without the need for sequential processing makes them extremely effective for various NLP tasks, including translation, text summarization, and ...

The transformer architecture has emerged as the predominant framework for deep learning, playing a pivotal role in the remarkable achievements of large language models like ChatGPT. Despite its ...

Abstract: To develop an accurate segmentation model for the prostate and lesion area to help clinicians diagnose diseases, we propose a multi-encoder and decoder segmentation ... branches of the model ...

To address these difficulties, we propose an enhanced Seq2Seq model named TLGA, where the hierarchical Transformer-BiLSTM encoder can capture long-range interactions and sequential semantics while the ...

Transformers contain several blocks of attention and feed-forward layers to gradually capture more complicated relationships. The task of the decoder module is to translate the encoder’s ...

I wanted to use an encoder decoder model with SwinTransformer as an encoder and bart-large as a decoder. I used VisionEncoderDecoderModel.from_encoder_decoder ...