Mar 10, 2021 · In Deep Learning, Many Complex problems can be solved by constructing better neural network architecture. The RNN (Recurrent Neural Network) and its variants are much useful in sequence to...

Mar 12, 2021 · Encoder-Decoder models were originally built to solve such Seq2Seq problems. In this post, I will be using a many-to-many type problem of Neural Machine Translation (NMT) as a running example....

Feb 13, 2025 · In this article, we studied the building blocks of encoder-decoder models with recurrent neural networks, as well as their common architectures and applications. We also addressed the most frequent issues we’ll face when using these models and how to fix them by using LSTM or GRU units, and the attention mechanism.

Oct 19, 2021 · We begin with background on neural encoder-decoder models with a focus on RNN-based models. A task in natural language generation falls under the class of sequence-to-sequence problems....

Aug 7, 2019 · The Encoder-Decoder architecture with recurrent neural networks has become an effective and standard approach for both neural machine translation (NMT) and sequence-to-sequence (seq2seq) prediction in general.

Aug 14, 2019 · The encoder-decoder model for recurrent neural networks is an architecture for sequence-to-sequence prediction problems where the length of input sequences is different to the length of output sequences.

Oct 23, 2023 · Through the analysis and summary of relevant literature, this study reveals the advantages of RNN and LSTM in sequence data processing; RNN structure is simple and effective, can process sequence input and output of any length, and can capture the time dependence in sequence data.

Standard feedforward neural networks cannot deal with variable length input. Compute the probability of a sentence, or given a sequence of words predicting the word that comes next. Mike hardly ever believes me . increase the size of the model.

Feb 28, 2019 · I've been unable to figure out the dimensions for an RNN encoder-decoder architecture. I understand how LSTMs work, but I am struggling to implement this one in Keras.

Aug 7, 2019 · Attention is proposed as a solution to the limitation of the Encoder-Decoder model encoding the input sequence to one fixed length vector from which to decode each output time step. This issue is believed to be more of a problem when decoding long sequences.