Questions tagged [sequence-modelling]
The sequence-modelling tag has no usage guidance.
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28 views
Best approach for online Machine Translation with few hundred of samples?
I want to implement a model that improves itself with the passage of time.
My main task is to build a machine translator (from English to Urdu).. The problem I am facing is that I have very little ...
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7 views
Why word embedding such as word2vec is not used as the output layer of a seq2seq decoder?
It would make sense to make the decoder predict a smaller embedding vector instead of softmax over a large dictionary.
The word having the most cosine similarity with the output embedding could be ...
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13 views
Predict the next best action based on previous lists of actions
I have the following problem. There is a software that I've written some time ago. Users enter customer's data in the system and there is a limited number of things (actions) that they can do/add - ...
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13 views
Make an LSTM model for each class separately
I have a dataset of some activities. The dataset contains the status of different sensors and the label of activity. T trained a model in Keras with the following architecture which models the ...
1
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1answer
27 views
Is “dataset size” and “model size” same thing?
I mean what is determine my model size, connection amount between layers and neurons, or size of my dataset?
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20 views
Literature on Sequence Regresssion
I have some rated time-sequential data and I would like to test if an ANN can learn a correlation between my measurements and ratings.
I suspect I could just try a CNN where 1 Dimension is time or an ...
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0answers
17 views
Language Model from missing data
I want to learn how a set of operations (my vocabulary) are composed in a dataset of algorithms (corpus).
The algorithms are a sequence of higher level operations which have varying low-level ...
0
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1answer
26 views
The Nature of Model Weights for Targeted Dropout
I am trying to figure out how to target certain model weights withtin my 1000x 1000 feed forward network in keras
...
6
votes
3answers
71 views
In sequence-to-sequence, why is the output of the decoder used as its input?
The basic seq-2-seq model consists of 2 parts: a recurrent encoder that compresses a sequence to a vector and decoder that unrolls the vector into the output sequence:
Why is the output, ...
0
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1answer
21 views
Can HMM, MRF, or CRF be used to classify the state of a single observation, not the entire observation sequence?
I learn that the Viterbi algorithm used for Hidden Markov Model (HMM) can classify a sequence of hidden states from the corresponding observations; Markov Random Field (MRF) and Conditional Random ...
0
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1answer
31 views
Why feeding the correct output as input during training of seq2seq models?
So, I've read about seq2seq for time-series and it seemed really promising, but when I went to implement it, all the tutorial I've found use the correct output as input to the decoder phase during ...
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66 views
How Seq2Seq with Bidirectional RNN works?
First of all the scope of the question is as follows - we have Sequence2Sequence architecture with:
Decoder: Bidirectional LSTM
Encoder: regular (single directional) LSTM
What I know:
When you ...
1
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1answer
96 views
Why can we approximate the joint probability distribution using the output vector of an LSTM?
In the paper, Contextual String Embeddings for Sequence Labeling, the authors state that
\begin{equation}
P(x_{0:T}) = \prod_{t=0}^T P(x_t|x_{0:t-1})
\end{equation}
They also state that, in the LSTM ...
4
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1answer
54 views
Transformer based decoding
Can the decoder in a transformer model be parallelized like the encoder?
As far as I understand the encoder has all the tokens in the sequence to compute the self-attention scores. But for a decoder ...
2
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0answers
66 views
Convolutional Sequence to Sequence Learning: Training vs Generation
I am struggling to understand the use of the Convolutional Sequence to Sequence (Conv-Seq2Seq) model. The image below is take directly from the paper and is the nearly canonical diagram of the ...
1
vote
1answer
176 views
Why do we need both encoder and decoder in sequence to sequence prediction?
Why do we need both encoder and decoder in sequence to sequence prediction?
We could just have a single RNN that, given input $x$, outputs some value $y(t)$ and hidden state $h(t)$. Next, given $h(t)$...
4
votes
1answer
687 views
Will attention based networks prevail over RNN and LSTM?
There is no point in picking one of the growing number of articles that come up in a web search for, "Deep learning attention networks," however the bold claims in Attention Is All You Need, Ashish ...
