Questions tagged [sequence]
The sequence tag has no usage guidance.
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Are the filtering problem and decoding problem the same thing?
Is there any distinction the filtering problem and the decoding problem?
Wikipedia's definition for a filtering problem is:
The problem of estimating the states or ideally the posterior distribution ...
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Running out of memory when training Keras LSTM model for binary classification on image sequences
I'm trying to come up with a Keras model based on LSTM layers that would do binary classification on image sequences.
The input data has the following shape: ...
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What's an appropriate datastore for variable length sequence data for PyTorch consumption?
I have a large number of sequences - potentially hundreds of thousands - each consisting of between 100 and 10,000 items, which each consist of about 5 floats.
I need a datastore that can rapidly ...
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Improving Wake-Word Detection Model Performance: Seeking Advice and Suggestions
I was assigned a task to train a wake-word detection model. Basically, it's a binary sequence classification model on audio samples where it should be 1 if it recognizes the wake word being said (e.g. ...
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Handle with very short and very long sequences with Neural Network
I am working on multi-class problem with sequences. My dataset is composed of sequences of data with different length.
E.g. 1500 labeled samples: 500 datapoint belongs to class A, 500 class B and 500 ...
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How is PCA applied to (one-hot encoded) DNA sequence data?
I realize some questions have been asked already about one-hot encoding for PCA. The answer seems to be along the lines of 'The PCA will run, but does not necessarily make sense.'
However, I have a ...
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Conditional density estimation for sequences using conditional random fields
I am looking to estimate the conditional distribution of the next observation $x_{t+1} \in \mathbb{R}_+$ of a discrete-time process, given the current observation and $l$ previous observations. I am ...
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User actions sequence classification
I have a training set where each row is a series of user actions on a website (logged in, sent an invoice, etc.) and times deltas in ms between these actions. Each row has a label — a corresponding ...
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Sequence models word2vec
I am working a data-set with more than 100,000 records. This is how the data looks like:
...
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Modeling the influence of events order on probability
The case is to model if the sequence of events influences the probability of binary target variable. We have for example five different events which occur in time (event: A,B,C,D,E). They can occur in ...
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Advantages of CNN vs. LSTM for sequence data like text or log-files
When do you tend to use CNN rather than LSTM (or the other way round) in classification or generation tasks of sequential data like text or log-data? What are the reasons for the decision and what ...
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Why use sliding window input features in deep learning?
I was reading through the DNABERT paper and found that their input features were k-mers. This is equivalent to using rolling/sliding window features in the other common family of sequential problem, ...
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Classification when the classification of the previous itens matter
I have a classification problem to solve, that seems to be common but I am struggling to find the name of this task and the best way to model this problem.
Suppose I have a series of events that are ...
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Why would a sequence-model vs n-gram model depending on ratio of Samples / Words per Sample
This ML tutorial from Google is analyzing the imdb reviews dataset to predict the tag positive or negative. When choosing a model
Calculate the number of samples/number of words per sample ratio.
If ...
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Give Variable Length input to LSTM
My input data consist of list of list. Both list have dynamic length for every example like below.
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Sequence prediction in Parent - Child dataset
We have a large collection of documents (D), each accompanied by a set of metadata (M). Within this collection, some documents act as parent documents and have multiple child documents. Both parent ...
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LSTM multivariate time-series with categorical, numerical and non-temporal inputs
I have a dataset that contains different data types and I'm working on a prediction task of dataset features with LSTM network, but I'm struggling in finding the right way to construct the neural ...
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Selecting an element in a sequence with self-attention networks
I have a doubt on I should set up the following problem:
Data:
My data is a tensor with shape (N, J, F) where N is the batch size, J is the sequence length, and F is the number of features of each ...
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How to use time-sequence data with "meta data" of single value per sample?
I'm, trying to estimate a fish weight by a time-sequence signal of the change in resistance when the fish goes through a gate with electrodes installed.
When the fish pass through the gate there's a ...
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Many To One LSTM - Can I Use the Same Sequence as Input from Previous Timesteps?
I'm new to LSTMs, and I'm trying to do a basic timeseries prediction using stock prices. However, I'm a bit confused as to how the LSTM is supposed to remember outputs from previous timesteps when it ...
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Why do position embeddings work?
In the papers "Convolutional Sequence to Sequence Learning" and
"Attention Is All You Need", positions embeddings are simply added to the input words embeddings to give the model a sense of the order ...
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Classification of sequential data
I'm currently trying to classify discrete sequential data into five classes with machine learning.
The setup is the following:
The actual object is filled with various properties, but to separate the ...
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Predict status of upcoming project milestones with intermediate activities
I have data of 100+ project data. Each project has about 175 sequential activities from start to end. There are approximately 7 key milestones between those 175 activities that we want to predict. ...
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predictions based on irregular repeated measures?
I need to make a model that predicts certain medical outcomes based on the answer to health-related questionnaires. Providers have patients fill out these questionnaires more than once, at irregular ...
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Can MLP model sequential data?
When modeling sequential data, RNNs are introduced as an improvement of MLP as they can model the time dependency between the inputs. It is said that feeding the last N data points in the sequence to ...
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Loss on whole sequences in Causal Language Model
I'd like to know, from an implementation point of view, when training a Causal Transformer such as GPT-2, if making prediction on whole sequence at once and computing the loss on the whole sequence is ...
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Techniques for early binary classification from sequences revealed over time in a low data environment?
We have data with objects, each of which has a series of events. A series is 1-50 events, revealed over time (a few months). These objects have events come in at different times during a season, so ...
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LSTM as learned positional encoding for vor variable sequence length input
I'm solving a classification task on a time-series dataset.
I use a Transformer encoder with learned positional encoding in the form of a matrix of shape
$\mathbb{R}^{seq \times embedding}$.
...
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Forecasting on multivariate time series containing quaternions
I have a multivariate time series containing 3D position data ($x,y,z)$ and orientation data (as quaternions) obtained from motion sensors.
My goal is to forecast the future position/orientation, and ...
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LSTM to multivariate sequence classification
How can I train multivariate to multiclass sequence using LSTM in keras?
I have 50000 sequences, each in the length of 100 timepoints.
At every time point, I have 3 features (So the width is 3).
I ...
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What is the formal category of problem described by identifying consecutive occurrences of attributes in records?
Apologies for the garbled title, I'd really need to know the answer to the question before I could phrase it properly...
Let's imagine I've got a data set of football(soccer if you prefer) match ...
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Given daily sequence of events with only event ID labels (alphanum strings), what algorithms can be used to detect sequences that are outliers?
For example, the data might be something like this:
...
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ML Modeling approach for Event data
I have this two dataset(image below).The one on the left shows events and the right is the alarm data.
Goal : Using the two datasets, after any number of events, an alarm can be triggered.I'd like to ...
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1D Sequence Classification
Cross-post from https://stackoverflow.com/questions/71752744/1d-sequence-classification
I am working with a long sequence (~60 000 timesteps) classification task with continuous input domain. The ...
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Algorithm for segmentation of sequence data
I have a large sequence of vectors of length N. I need some unsupervised learning algorithm to divide these vectors into M segments.
For example:
K-means is not suitable, because it puts similar ...
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Sequence Embedding using embedding layer: how does the network architecture influence it? [closed]
I want to obtain a dense vector representation of protein sequences so that I can meaningfully represent them in an embedding space. We can consider them as sequences of letters, in particular there ...
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Predict indices of text using deep learning
I want to predict the start and end indices of text where a certain type of propaganda technique is used like smears, name-calling, loaded language etc. Some examples from the dataset are:
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Deep Learning Model to predict sum of Sequences based on flag value
I am trying to Predict Sum of the Sequence based on flag but my model is not able to converge.
for each time stamp, include the first element in sum if second number is 1 in
Sequence.
Example
...
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Comapring hidden markov models
Given a set of sequence transitions, there are different orders of hidden markov models that can be fitted to a dataset.
Is there any test to determine which is the best model for a given sequence ...
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What algorithms are good to predict next numbers?
Let's consider we have several hundreds of numbers like ( 1, 2, 5, 8, 7, 15, 19, 8, 4, 6, ...) those are closed numbers of a stock on consecutive days for example. I like to know what algorithms are ...
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How to compare different forecasting models over different time horizon?
Developed multiple Models with AR, ARIMA, VAR; LSTM , SARIMA.
Now, the purpose is to find out which model performs best on a given use case with different time horizons.
The time series data is ...
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How does data shuffling work when LSTM is involved?
TIL that when using the LSTM layer, the states are remembered throughout the same batch. When using stateful LSTM, they can be even remembered outside of the batch.
The first realization gave me a ...
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Machine learning Classification model for binary input and output data
I have a large longitudinal dataset with 5 minute granularity for a period of around 30 months from thousands of households. I would like to classify them using a binary output (0/1) based on the ...
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Sequence data vs time series data
What is the difference between sequence data and time series data?
My understanding is that sequence data is any data where the order matters and time series is a special type of sequence data ...
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Text generation with deep neural network?
For my master's project, I have to build a deep learning model for text generation: the model learns on a set of sentences, then it generates new sentences based on those from which it learned.
I ...
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Seq to Seq modelling - ML Algorithms to use
Am new to ML. While I learnt the classical ML concepts like Linera regression, Logistic regression, Boosting and tree based techniques, now am slowly trying to learn Deep Learning techniques like CNN, ...
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Can bidirectional RNN use variable sequence length?
A bidirectional RNN consists of two RNNs, one for the forward and another for the backward sequential directions, which outcome is concatenated at each time step. Would this configuration restrict the ...
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LSTM with return_sequences - "Training a model on multiple timesteps simultaneously"
So I'm following Tensorflow's LSTM/time series tutorial and there's something I don't understand. I do understand what happens with return_sequences on/off, however,...
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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 ...
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