Questions tagged [sequence-to-sequence]
The sequence-to-sequence tag has no usage guidance.
What type of neural network could emulate a binary to HTML conversion tool?
I've got a problem, which I thought could be solved by using a neural network: I've got a binary file and a tool that converts that file into a readable html file (probably a text file as well). How ...
Can Sequence to sequence models be used to convert code from one programming language to another?
Is it possible to convert a code from one language to another using sequence to sequence programming. if not sequence to sequence, which algorithm can i use to do this?
Predict output sequence one at a time with feedback
I would like to solve the following classification problem: Given an input sequence and zero or more initial terms of the true output, predict the next term in the output sequence. For example, my ...
Is this a problem for a Seq2Seq model?
I'm struggling to find a tutorial/example which covers using an seq2seq model for sequential inputs other then text/translation. I have a multivariate dataset with n number of input variables each ...
Encoder-Decoder Sequence-to-Sequence Model for Translations in Both Directions
Is it possible to use a pre-trained sequence to sequence encoder-decoder model which translates an input text in source language to an output in target language to do an inverse translation? That is, ...
On-the-fly seq2seq: starting translation before the input sequence ends
By now anybody even remotely familiar with RNNs has been exposed to the famous figure representing variants of such networks: However one type that is missing is one where there would be a partial ...
Neural network outputting same result for all inputs
I'm building an encoder-decoder neural network in Keras for sequence generation. The specific task is to try and change the styles of the text. Both my encoder and decoders are LSTMs with latent ...
Keras: Softmax output into embedding layer
I'm trying to build an encoder-decoder network in Keras to generate a sentence of a particular style. As my problem is unsupervised i.e. I don't have the ground truths for the generated sentences, I ...
What are the benefits and tradeoffs of a 1D conv vs a multi-input seq2seq LSTM model?
I have 6 sequences, s1,..,s6. Using all sequences I want to predict a binary vector q = [0,0,0,1,1,1,0,0,0,1,1,1,...], which is a mask of the activity of the 6 sequences. I have looked at seq2seq ...
Training Encoder-Decoder using Decoder Outputs
I am trying to build an encoder-decoder model for a text style transfer problem. The problem is I don't have parallel data between the two styles so I need to train the model in an unsupervised ...
Do we really need <unk> tokens?
I am wondering, do we really need <unk> tokens? Why do we limit our vocabulary? Is it for speed? Accuracy? If we disable all limitations, what do you ...
Very long sequence in neural networks
Beginner's question regarding sequences in neural networks: suppose I have classification problem that looks like: X = very long sequence of varying length. Y = class (assume for simplicity y=0/1). ...
I have following sort of data coming every day: (0)(3,4,5)(6,9,1)(5,35,12,232) (1)(5,1,4)(6,2)(12,54,12,43)(8,23,65) (2)(6,7,2)(34,3) (3)(4323,23,12,4543) (4)(987,32,324,23,224,12,213,21)(1,2) (...
How to pad real-valued sequences
I have several sequences of univariate real-valued time-series data. The sequences are of different lengths and right now I cannot batch them and feed them to a network. What is the correct procedure ...
How/What to initialize the hidden states in RNN sequence-to-sequence models?
In an RNN sequence-to-sequence model, the encode input hidden states and the output's hidden states needs to be initialized before training. What values should we initialize them with? How should we ...
How do attention mechanisms in RNNs learn weights for a variable length input
Attention mechanisms in RNNs are reasonably common to sequence to sequence models. I understand that the decoder learns a weight vector $\alpha$ which is applied as a weighted sum of the output ...
Input for LSTM for financial time series directional prediction
I'm working on using an LSTM to predict the direction of the market for the next day. My question concerns the input for the LSTM. My data is a financial time series $x_1 \ldots x_t$ where each $x_i$...
Why do we need to add START <s> + END </s> symbols when using Recurrent Neural Nets for Sequence-to-Sequence Models?
In the Sequence-to-Sequence models, we often see that the START (e.g. <s>) and END (e.g. </s>) symbols are added to ...