Questions tagged [machine-learning]

For questions related to machine learning (ML), which is a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data). ML is usually divided into supervised, unsupervised and reinforcement learning. Deep learning is a subfield of ML that uses deep artificial neural networks.

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30 views

How to measure the significance of an input feature for the output of a linear layer in a neural network

Suppose I have a simple linear layer $y = xA^T + b$ that is part of a neural network trained on some dataset. The weight matrix $A$ for this layer has the shape ...
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1answer
64 views

Backpropagation after N sequential input-output pass

I'm trying to train a Neural Network in a particular situation -- similar to a genetic algorithm domain as far as I know. I have to run a simulation with a length of $K$ steps. I have a neural network ...
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25 views

Material(s) for understanding "image channels"

I am pretty confused about the concept of "image channels". I want material that explains the concept of channels from scratch to whatever is required to understand their role in machine ...
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21 views

Is there a term for performance metric like prediction time on a new/unseen example?

The performance entry on Google's machine-learning glossary doesn't mention prediction time on a new/unseen example which is important for production use. Is there a term to refer to that metric?
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54 views

How do I know what a good mean absolute error value is? [closed]

I have just run an MAE calculation for my machine learning models and the results show: SVM MAE = 28.850 deg. Random Forest MAE = 33.832 deg. How do I know what a good MAE value is? What is the ...
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46 views

What is the meaning of R2 appearing as a negative in the RandomForestRegressor?

Machine learning model was created by reading an Excel file where data was stored. I applied RandomForestRegressor to create a model that predicts the size of the sieve particles according to pressure,...
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24 views

ReLU function converging to local optimum in one case and diverging in the other one

I implemented a simple neural network with 1 hidden layer. I used ReLU as activation function for the hidden layer and the output layer just uses the linear function. To check my implementation I ...
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1answer
78 views

How to create a neural network from a set of equations?

Say I have these equations: $$x_1 = x_2 + 2y_1 + b$$ $$x_2 = y_2 + c$$ $$y_1 = z + a$$ $$y_2 = y_3 + d$$ $$z = z_1 + e$$ $x_1$ depends on $x_2$ (depends on $y_2$ (depends on $y_3$)) and $y_1$ (depends ...
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Does regularization just mean using an augmented loss function?

We need to use a loss function for training the neural networks. In general, the loss function depends only on the desired output $y$ and actual output $\hat{y}$ and is represented as $L(y, \hat{y})$. ...
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32 views

What to do when model stops learning after some epochs

I am training a segmentation model on 3D data, after around 170 epochs which took around 4 days, I notice the model is no more learning and the dice score is at 0.51. What is the best approach at this ...
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12 views

Performance metric for multi aspect extraction and sentiment analysis

I created a model that extracts aspects from reviews and predicts the associated sentiments. I'm now trying to evaluate the model. I tried many different approaches because there are no real true ...
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59 views

Why is the exponential loss used in this case?

I am reading the paper Tracking-by-Segmentation With Online Gradient Boosting Decision Tree. In Section 2.1, the paper says Given training examples, $\left\{\left(\mathbf{x}_{i}, y_{i}\right) \mid \...
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32 views

Is there a way to select the subset of most important features using PCA?

Is there a way to select the most important features using PCA? I am not looking for the principal components with the highest scores but a subset of the original features.
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Time series forecasting for multiple objects with common features

I know the title of this question may raise an eyebrow, but I can't find the technical terms to define or investigate the actual problem. To demonstrate my problem with a simple hypothetical scenario: ...
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33 views

How to calculate cosine similarity for classification when you have say 10000 samples belonging to two classes have a bunch of samples

Does anyone have experience with using Cosine Similarity for text classification? I see a number of articles on how to find cosine similarity between documents using Doc2Vec, Gensim, etc. I have a ...
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Predicting yes/no questions with input query

If I have information about a person e.g. their lifestyle, how big is their house, where they live, their income history, I can build a model to predict their future income. But what if my task is not ...
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17 views

How can I take continuous video input into my model?

Let's say I have designed an ML model that can take video input of a dog running around and give the breed of the dog as output. However, I do not want to wait for the video to finish before it is ...
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19 views

What's the effect of increasing hidden nodes?

Topic Demarcation I find many topics on "how to choose the number of hidden nodes". I'm not interested in the answer to that question. What I learned I learned, that if you increase the ...
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9 views

How to estimate the number of training samples to train an HMM with the Baum-Welch algorithm?

Is there a thumb rule that tells us how many data samples an HMM needs to be trained by the Baum-Welch algorithm?
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14 views

How to deal with unbalanced data in multilabel classification problem

I have 3 possible solutions, but I am not sure if they are good. I think they are a bit clunky (especially 1st and 2nd). Use multiple small models. So instead of having the model that can tell you ...
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14 views

End-to-end learning using LSTM-AE

I want to use prediction models like LSTM-AE to predict time-series data. The feature that the neural network should learn is in frequency between 40-60Hz. So, in order to learn the feature more ...
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3 views

Ensure trained word embeddings get high similarity with particular words

I am trying out my hand at training a Word2Vec model using gensim. I made a simple training file that basically had just one line repeated multiple times ...
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Is optimizing weighted sum multi objective tasks considered a multi-task learning?

I have two sequence prediction tasks, finding $\vec{\pi} \in \Pi$ and $\vec{\psi} \in \Psi$. Each sequence has its own objective function, i.e. $f_1(\vec{\pi})$ and $f_2(\vec{\psi})$. The input for ...
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1answer
29 views

How can I address missing values for LSTM?

I'm a student and writing my first paper for submission on conference. I have a question there is a dataset below. this is temporal-spatial dataset. ...
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36 views

What is meant by decoding in a Hidden Markov Model?

HMM contains two types of states: observable and hidden. Let $\{ h_1,h_2,h_3,\cdots,h_n\}$ be hidden states and $\{o_1,o_2,o_3,\cdots, o_m\}$ be the observable states. Suppose the $n^2$ transition ...
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128 views

Computational complexity of a CNN network

In the following network, the convolution operations of convolutional blocks are performed by three 1-D kernels with the sizes 8, 5, and 3 respectively along with stride equal to 1. The final network ...
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1answer
36 views

What is a better approach to perform predictions of time-series several values ahead?

Suppose one has a time series (univariate or multivariate) and the goal is to predict values of these series several steps ahead. I see two possible strategies: Create a model (recurrent, ...
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1answer
78 views

What does it mean by overfitting the test set?

Consider the following statement from p14 of Naive Bayes and Sentiment Classification While the use of a devset avoids overfitting the test set, having a fixed training set, devset, and test set ...
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70 views

Can some of the weights be fixed during the training of a neural network?

Is it possible to exclude specific layers from the optimization? For example, let's say I have an input layer, 2 hidden layers, and the output layer. I know there is a perfect solution for my problem ...
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21 views

Multiple Entities, Multivariate, Multi-step - Time Series Prediction - Python

My goal is to create a time series model with Multiple Entities - I have multiple products with pre orders and they all have the a similar bell shaped curve peeking at the release date of the product ...
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3answers
126 views

Can people use neural networks without providing the set of training data?

It seems that neural networks (NNs) can be applied to supervised learning, unsupervised learning and reinforcement learning. Some people even train neural networks without the set of training data. If ...
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25 views

Machine learning to find fewest number of "puzzle pieces" that fullfill a certain requirement

Would machine learning be suitable for the following problem, and if so, what kind of learning? I have numerous puzzle pieces, all having a value for identical properties. Example of one puzzle piece: ...
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What are some of the ideas to solve Learning-to-choose problem?

Suppose I want to predict cats and dogs, but with a twist: the model can choose the image to predict. For example: Given a list of 10 images (with both dogs and cats), the model need to choose one ...
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16 views

Determining the value of error threshold in summarization of differences in multidimensional aggregates

I have been implementing an OLAP related journal "iDiff : Informative summarization of differences in multidimensional aggregates". In this paper, The author have proposed a methodology ...
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Is it possible to identify multiple queries/intents in an email, check if the reply has addressed all of those queries before sending email?

An email may contain multiple questions related to similar or distinct topics. The person responding the email needs assistance in detecting and informing if all of the questions have been addressed ...
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102 views

How to design a neural network with arbitrary input and output length?

I am trying to build a neural network that has an input of $n$ pairs of integer values (where $n$ is random) and a corresponding output of a binary array with length $n$. The input will be a set of ...
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85 views

Is there any model that is probabilistic but not statistical?

While studying about the n-gram models, I encountered the terms "statistical model" and "probabilistic model" several times. I got a basic doubt that will there be any ...
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17 views

How should an ML model architecture be designed for predicting the order of a sequence?

I've decided to create a model for predicting Formula 1 race results based on driver statistics, to try to improve my ML skills. The first problem I've encountered is the data type of the target ...
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18 views

Structure of machine Learning algorithm

I am experimenting on automation on video editing. The idea is obtaining "strange results" and experiments, not looking for any precise result. So, I want to design a simple neural network ...
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83 views

Taking a machine learning model to production\deployment

I've designed a machine learning model for the predictive maintenance of machines. The data used for training and testing the ML model is the data from various sensors connected to various parts of ...
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1answer
129 views

What is the meaning or implications of the rank of a dataset for machine learning algorithms?

Consider a dataset with $n$ training examples and $d$ features. Let $D_{n \times d}$ be the data matrix and $r$ be the rank of it. In matrices, rank $r$ is generally useful in Knowing the dimension ...
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1answer
29 views

Why is ancestral sampling used in autoregressive models?

I have been reading about autoregressive models. Based on what I've read, it seems to me that all autoregressive models use ancestral sampling. For instance, this paper says the following in Abstract: ...
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36 views

AI model to predict/generate person's image

I want to make a model that predicts person's shape depending on his son's image. My plan is to create a dataset and each data point in it consists of two images; One for the father or mother and one ...
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92 views

How would we get a good estimation of the asymptotic performance of machine learning algorithms?

The following question is from the webbook Neural Networks and Deep Learning by Michael Nielson: How do our machine learning algorithms perform in the limit of very large data sets? For any given ...
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12 views

Evaluation metric for time-series anomaly detection

My dataset is time-series sensor data and anomaly ratio is between 5% and 6% 1. For time-series anomaly detection evaluation, which one is better, precision/recall/F1 or ROC-AUC ? When empirically ...
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1answer
73 views

Is $(y_i - \hat y_i)x_i$, part of the formula for updating weights for perceptron, the gradient of some kind of loss function?

A post gives a formula for perceptron to update weights I understand almost all the parts of it, except for the part $(y_i - \hat y_i)x_i$ where does it come from? Is it the gradient of some kind of ...
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75 views

Is my flowchart a good representation of the perceptron learning algorithm?

I made a flowchart for a simplified perceptron leaning algorithm. Here is the process of the learning algorithm. Initialize the weights first. Get a training example randomly and make a prediction. ...
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11 views

Are supervised learning object recognition models appropriate for the challenge of recognizing dynamically generated terrain features in a game?

I decided to practice applying object recognition with TensorFlow for an interesting application this weekend. The application I chose was to recognize enemies in a game world, and as more of a ...
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39 views

Dilated CNN - How to deal with odd lookbacks?

I'm currently working with a dilated CNN to solve a regression problem. I am trying to forecast 24 timesteps ahead based on 6, 12 and 24 values (lookback). However I am not sure which dilation rate ...
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11 views

What are the typical things in data that I would have to look for, when implementing survival models using machine learning?

Problem Scenario I am working on an industry-specific problem focussed on predicting the failure of a seal/gasket in the given time interval(T) in a high-pressure-compression environment. Whenever ...

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