The authors of the 2017 paper showed that most competitive neural sequence transduction models have an encoder-decoder structure, where the encoder maps the input sequence of symbol representations to ...

We will use PyTorch + Lightning to create and optimize an encoder-decoder transformer, like the one shown in the picture below. Code a Position Encoder Class From Scratch!!! The position encoder class ...

Example - seq2seq model It creates output using autoregressive decoding to generate output sequence. Control token can be used to control the decoding ...

There is a new paper by Google and Waymo (Scaling Laws of Motion Forecasting and Planning A Technical Report that confirmed ...

The autoregressive (AR) models, such as attention-based encoder-decoder models and RNN-Transducer, have achieved great success in speech recognition. They predict the output sequence conditioned on ...

This comprehensive guide delves into decoder-based Large Language Models (LLMs), exploring their architecture, innovations, and applications in natural language processing. Highlighting the evolution ...

The Main Components of Encoder-Decoder Architectures: Encoder: The encoder processes the input data and compresses the information into a context or a state. This state is supposed to capture the ...