Questions tagged [sequence]
The sequence tag has no usage guidance.
119
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What's the right machine learning approach to mark rubrics based on sequences of data?
I'm a teacher and I'm working on a pet project to help streamline some of my assessment workflows for my students. One of those workflows is gathering data on student progress in the form of a rubric ...
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Calculating prediction confidence from a sequence of token-level confidences
I am working with OCSR (optical chemical structure recognition) models, and they output a sequence of token-level confidences. I am looking for a method of summarising these token-level confidences ...
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Predict best chess move using RNNs
I am trying to do a project with AI: in which during any certain moment of a chess game i can predict, using a RNN trained on a kaggle dataset, the best possible move that i can make.
I am having ...
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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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51
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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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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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128
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Which should we choose: sequence-model vs n-gram model and why does it depend 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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62
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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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282
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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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54
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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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305
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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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883
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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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59
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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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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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53
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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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139
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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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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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80
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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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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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136
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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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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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544
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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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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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41
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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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242
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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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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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How can I predict the last element of the fixed length=8 sequence after I get each element?
There are fixed length lists [X1, X2, X3, X4, X5, X6, X7, X8] like this.
I have many lists like them from the past. In the future, I will get new element of current list on weekly bases. one new ...
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389
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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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113
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Create sequence for a Conv1D layer
Im studying the following tutorial on the Keras website and I'm trying to understand how to create a sequence for a Conv1D layer.
This is their method:
...
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34
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Time-Series analysis with small data set, but long sequences
I'm working on a time-series classification problem. There are 3 classes. Dataset consists of 6 sequences from each class (total 18 sequences). Each sequence is 19,000 in length. What are some time-...
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23
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Find differences of two protein sequence files
I have two separate files A and B of more than 100000 records of protein sequences. Now I need to find the sequences that are in ...
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133
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Random Sequence Predictions
Can machine learning algorithms predict random number generators.
key
A= 1 2 3
B= 4 5 6
C= 7 8 9
Example to catch a number sequence
4 8 8
I would select B C C
That would give me 27 number combinations
...
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419
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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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Choosing a model for input: categorised, weighted sequence, output: binary variable
What would be an appropriate model for predicting a binary target variable, given a weighted sequence?
Sequences will be reasonably short, typically between ~ 1 and 5 elements.
Illustrated example
Say ...
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In sequence models, is it possible to have training batches with different timesteps each to reduce the required padding per input sequence?
I want to train an LSTM model with variable length inputs. Specifically I want to use as little padding as possible while still using minibatches.
As far as I understand each batch requires a fixed ...
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What's the difference between sequence preprocessing and text preprocessing in Keras?
In Keras, we mainly have three types of preprocessing, i.e., sequence preprocessing, text preprocessing, and image preprocessing. However, for me, I think the meanings of the word "sequence" ...
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458
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How to train a model on top of a transformer to output a sequence?
I am using huggingface to build a model that is capable of identifying mistakes in a given sentence.
Say I have a given sentence and a corresponding label as follows ->
...
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128
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Machine learning for circular sequences
My data are sequences of real numbers $a_0,a_1,...,a_{n-1}$. The length of a sequence is fixed and equals $n$. Each sequence is mapped to a real number $y$ and I want to predict $y$ given the sequence....
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Recommender Model for Human Action in Income Protection
Problem Domain
I'm working on a project that involves building a model to provide recommendations on the next best step for Human supervisors to take on income protection claims.
Income protection is ...
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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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493
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How to convert DNA sequences in FASTA format to OneHot Encoded Pandas Dataframe for Neural Networks?
DNA sequences in FASTA format look like:
CATGCATTAGTTATTAATAGTAATCAATTACGGGGTCATTAGTTCA...
I am trying to convert them into one-hot encoded data in a Pandas ...
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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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What is External representation of time in Sequential learning?
I am reading the literature on sequential learning and it is often mention that in order to handle sequential/temporal data, there two categories of work in sequential learning
External ...
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263
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Predicting sequence element based on the previous M and the following N elements
I have an array of sequences of equal length, each sequence contains 300 numbers (M=300). Each element in a sequence is a number from 1 to 9:
...
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154
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Predicting next element of a sequence given small amount of data
I have data of bank branches and amount of revenue they have generated in a month. The data looks like this:
I am tasked to find the expected revenue for the branch for the next month using machine ...
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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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