If they are very long, you might need a more complex encoder-decoder architecture that can handle the long-term dependencies and avoid information loss ... a transformer model, which uses self ...

I've been following this to implement a bert2bert seq2seq model which works pretty well. Now I would like to change this to mbart (facebook/mbart-large-50) instead of bert. I'm very new to this, but ...

Similar problems also confuse the implementation of the decoder. How to reduce the problem of information loss in the encoder-decoder model is critical for text generation. To address this issue, we ...

It is of great importance to reduce this radiation exposure by using low-dose CT (LDCT) acquisition, which is effective, but reconstructed CT images tend to be degraded, leading to the loss ... an ...

An Encoder-Decoder model is a fundamental architecture in the field of deep ... Training Objectives: Decoders are often trained using various objectives such as cross-entropy loss for sequence ...

As the model is trained, the loss function values decrease ... Considering the effectiveness of atrous convolution, this study adds ASPP module in the middle of encoder and decoder. By using the ...