Questions tagged [recurrent-neural-net]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
0
votes
0answers
11 views

What is reranking?

I am pretty new to semantic parsing and that stuff but I have to give a presentation about reranking. Can you give a good definition, what reranking does? I think it is related with the parsing ...
2
votes
1answer
34 views

What are the exact differences between Deep Learning, Deep Neural Networks, Artificial Neural Networks and further terms?

After having read some theory I am getting a bit confused about the following terms: Deep Learning Deep Neural Network Artificial Neural Network Feedforward Neural Network So, what seems clear to me ...
0
votes
1answer
11 views

RNN model weather data for predicting temperature [closed]

I have a 30 years daily weather data. Data Sets parameter are rainfall, cloud amount, humidity, sunshine and temperature. Now, I want to build predictive model. rainfall, cloud amount, humidity and ...
0
votes
0answers
9 views

Why/when is someone resetting a recurrent neural network necessary?

This is my first time implementing a recurrent neural network, and I'm confused as to why resetting the node activations is necessary. When is it necessary to reset node activations? Specifically, ...
1
vote
1answer
26 views

What is the best way Reinforcement learning, RNN or others to predict the best action we have to take to maximize sales?

I have a dataset composed of few features : customerId, actionDay1, SalesDay1, actionDay20, SalesDay20, actionDay30, SalesDay30 action can be : call email face ...
0
votes
0answers
3 views

How calculate computation time for each part of the network

I want to report how much times it takes to compute each specific part of the network in a batch (forward and backward time). For example, in this paper they've reported RNN, softmax, and optimization ...
0
votes
0answers
30 views

2019 - Bleeding edge Reinforcement Learning techniques?

I've built an RL agent using the following: Full Rainbow: Double Q-Learning (allow target network to rate the Q-score of the action selected by online network, use this score as a TD target) ...
0
votes
0answers
19 views

Time Series Forecasting with RNNs

I'm attempting to develop a recurrent model to forecast the value one step into the future (i.e., $x_{t+1}$), given its history $(x_{t-h},\cdots,x_{t})$, where $h$ is a fixed hyperparameter for the ...
0
votes
0answers
15 views

Is Elmo equivalent to Fasttext+Bi-directional GRU?

From what I have read, Elmo uses bi-directional LSTM layers to give contextual embeddings for words in a sentence. So if I use a bi-directional LSTM/GRU layer over Fasttext representations of words, ...
0
votes
0answers
11 views

Difference between globalmaxpoolin1d() and attention layer

What's the difference between globalmaxpoolin1d() and attention layer?
0
votes
3answers
52 views

How to know when to stop trainning a deep network?

I've been training several auto encoders containing two GRUs as encoder and decoder during last year. It occurred to me that ...
0
votes
0answers
38 views

In an RNN, if the gradients don't vanish for long/distant terms, won't the derivative of the error be either divergent to infinity or oscillatory?

P.S. Crosss posted here- https://stats.stackexchange.com/questions/413843/in-an-rnn-if-the-gradients-dont-vanish-for-long-distant-terms-wont-the-deriv, as I've got no answer, I'm asking here: In my ...
0
votes
0answers
12 views

Generalization of RNN/LSTM/GRU… model

Given a time-series prediction with a Recurrent Neural Network (doesn't matter if LSTM/GRU/...), a forecast might look like this: to_predict (orange) was fed to the model, predicted (purple) is the ...
1
vote
0answers
26 views

TensorFlow: how to restore pre-trained meta model and pass it's weights and biases to the optimizer?

I trained a model on a specific dataset and saved it as a meta, I want to restore the model and use its weights and biases on another dataset the code isn't mine but I'm trying to restore the ...
0
votes
0answers
11 views

How can memory networks perform well in lists/set type?

I was reading this paper about memory networks. As I understood, memory networks can give output in a word. But on Babi dataset's 'list/set' task, its accuracy was almost 80%. What have I ...
0
votes
0answers
25 views

Does an LSTM model with one hidden layer has much advantages over a RNN or NN?

Does an LSTM model with one hidden layer has much advantages over a RNN or NN? Cause the network is not deep/large
0
votes
1answer
19 views

How can I train a many-to-one RNN with an array of 2D matrices?

I have eye tracking data for every word of a novel. Features for every word is given separately. I want to take groups of 100 words to make a sample and then use each of these samples as a single ...
0
votes
0answers
11 views

Why are reservoir computer so useful for hardware implementations

I often read (e.g. here or in this question) that Reservoir Computer (RC) are useful in the field of Neuromorphic Computing where they can serve as efficient implementations of neural networks in ...
0
votes
0answers
11 views

What will go wrong if we apply linear or other types of regression to translate sentences between two languages?

Disclaimer: I asked the question at https://stats.stackexchange.com/questions/408463/what-will-go-wrong-if-we-apply-linear-or-other-types-of-regression-to-translate, but didn't get any response, so I'...
0
votes
0answers
29 views

What's the difference between hidden layer size and sequence length in RNN and LSTM?

I have been exploring RNNs in keras implementations. In the LSTM layer we have to provide a hidden layer size and also a sequence length. My question is, what does hidden layer size correspond to and ...
1
vote
0answers
74 views

How to apply an RNN to forecast non-stationary time series?

Is it possible to predict a time series which is non-stationary, in the sense that, the dependent variable Y have an increasing trend? Therefore, the highest value of $Y$ in the training set may be ...
0
votes
1answer
39 views

LSTM input and output for sentiment analysis

I'm studying this LSTM network: https://www.kaggle.com/paoloripamonti/twitter-sentiment-analysis ...
0
votes
1answer
19 views

Why don't we gradually update the activation parameters in RNN from one activation to the next as the network is learning more?

I'm very new to (unidirectional, vanilla) RNN and sequence modeling in general, and all I understood about the motivation on having the connection between two successive hidden layers/activation is ...
0
votes
0answers
64 views

LSTM Predict values out of test

I'm trying to predic stock values from a dataset, for example: Google stock. I have this easy model. ...
2
votes
1answer
24 views

How to perform polynomial landmark detection with deep learning

I am trying to build a system to segment vehicles using a deep convolutional neural network. I am familiar with predicting a set amount of points (i.e. ending a neural architecture with a Dense layer ...
0
votes
0answers
15 views

Strategy for “forcing” number of labels in seq2seq predictions with Keras?

I'm trying to train a seq2seq model that for every timestep in a given timeseries sample will output 1 of 6 possible labels. Furthermore, the training data is constructed in such a way that Each ...
1
vote
0answers
79 views

One hot encoding as input to recurrent neural networks

I'm trying to predict next label in a pattern based on previous labels using recurrent neural network. In total I have 100 labels Example of input pattern: ...
0
votes
0answers
54 views

Using an RNN to predict fantasy football results

So I have a couple questions about the design of a neural network. I'm trying to create a neural network to predict the number of fantasy points a player will score in a given week. First of all, I ...
1
vote
0answers
50 views

Sequence classification using oneClass SVM

In the code below, I'm using a sequence to sequence approach as a prediction model for anomaly detection, The data set I'm working with is ADFA-LD. The training phase is done using only normal ...
1
vote
1answer
107 views

How to feed a table per timestamp to LSTM neural network?

I have a time-series dataframe like this ...
0
votes
0answers
26 views

What encoding to use for my musical vectors?

I'm trying to build a music recommendations system using an encoder-decoder sequence-to-sequence architecture using keras. My dataset comprises of playlists containing songs represented as a 13-...
1
vote
1answer
30 views

Working ofLSTM with multiple Units - NER

I am trying to understand working of LSTM networks and kind of not clear about how different neurons in a cell interact each other. I had a look at a similar question, but still not clear about few ...
1
vote
0answers
20 views

Adding context in a sequence to sequence problem

The encoder of a seq2seq model is meant to generate a conditioning context for the decoder, as mentioned here A RNN layer (or stack thereof) acts as "encoder": it processes the input sequence and ...
1
vote
0answers
15 views

Why is MLP working similar to RNN for text generation

I was trying to perform text generation using only a character level feed-forward neural network after having followed this tutorial which uses LSTM. I one-hot encoded the characters of my corpus ...
0
votes
0answers
18 views

My question is about dependency between hidden states for Back Propagation Through Time in RNN

In one video lecture, professor Ali Ghodsi of University of Waterloo says that the first node of S(t)(hidden state of RNN at time t) has an effect only on the first node of S(t+1)(hidden state of RNN ...
1
vote
1answer
226 views

how to apply MC dropout to an LSTM network keras

I have a simple LSTM network developped using keras: ...
2
votes
2answers
270 views

Unnormalized Log Probability - RNN

I am going through the deep learning book by Goodfellow. In the RNN section I am stuck with the following: RNN is defined like following: And the equations are : Now the $O^{(t)}$ above is ...
1
vote
1answer
30 views

How to estimate the not available observation in time series data?

Suppose, I have a 30 seconds time-step observations of sports data, in some of the intervals the game was partially/fully stopped. I'm trying to prep the data for a time series analysis. Is it ...
2
votes
1answer
40 views

How to create a language translator from scratch?

I want to create a translator which can translate English, Korean and Tamil sentences into English sentence, I tried googletrans but is there any way to create something better than that using DL and ...
1
vote
0answers
20 views

Reinforcement learning - generating a matrix of continuous values with varying size for test data generation

Currently, I am using RL A3C algorithm for test data generation, where for a set of 30 functions written in C (mostly basic algorithms like Prime number checks, triangle validity, etc.) I try to ...
0
votes
1answer
48 views

Accuracy and Loss in MLP

I am trying to explore models for predicting whether the a team will win or lose based on features about the team and their opponent. My training data is 15k samples with 760 numerical features. Each ...
1
vote
1answer
34 views

principles of time series analysis by neural network models

I can understand for speech signals, words are correlated and therefore one should have a reason to believe that recurring NNs or LSTMs could predict by running some complex algorithm with weights and ...
0
votes
0answers
21 views

Is this code correct for a sequential model for time series pattern prediction Keras

posting this to stack exchange DS as I have also seen people answering keras related questions here! I have a question about pre-processing data in order to enter it into a sequential model in keras ...
0
votes
0answers
34 views

How to training the recurrent recommender system with LSTM?

Recently, I read a paper about recurrent recommender system, I am very curious about how it training its network. Assume I have the Netflix dataset as ...
0
votes
1answer
38 views

Is the LSTM share the same model parameter in each block?

I learn the LSTM recently, and a little bit confuse about the model parameters about LSTM, The follow is the LSTM structure And it is equation as (I slightly ignore the bias in each equation): $$...
0
votes
1answer
45 views

ML technique to predict next performance anomaly

I would like a direction for a ML technique to predict when an anomaly will occur in a system, like when the CPU use differs from a normal state. My data consists of system metrics collected from AWS ...
1
vote
2answers
337 views

LSTM sequence prediction: 3d input to 2d output

I have this LSTM model ...
2
votes
1answer
349 views

How to reshape data for LSTM training in multivariate sequence prediction

I want to build an LSTM model for customer behaviour. It's the first time for me working on a timeseries, so some concepts are not clear to me at all. My prediction problem is multidimensional, ...
0
votes
0answers
34 views

Neural Network Architecture for batch of time series data

Let's say I have a data set which is a 2-Dimensional Matrix as the input and I want to predict either 0 or 1 with regard to the entire 2-D matrix. Now each row in the 2-D matrix is a time series, i.e....
4
votes
0answers
63 views

LSTM Long Term Dependencies Keras

I am familiar with the LSTM unit (memory cell, forget gate, output gate etc) however I am struggling to see how this links to the LSTM implementation in Keras. In Keras the input data structure for X ...