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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3 views

Using tensor networks as machine learning models

Tensor networks (check this paper for a review) are a numerical method originally introduced in condensed matter physics to model complex quantum systems. Roughly speaking, such systems are described ...
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Computing latent representation for multi-domain regression/classification

Suppose I have a dataset with (X, Y) training samples where X is a 1 dimension, and Y is also 1 dimension. Example: if this is a housing price dataset, X would be an area in square feet, and Y would ...
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Why is there more than one way of calculating the accuracy?

Some sources consider the true negatives (TN) when computing the accuracy, while some don't. Source 1: https://medium.com/greyatom/performance-metrics-for-classification-problems-in-machine-learning-...
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Why is the loss of one of the outputs of a model with multiple outputs increasing while the others are decreasing?

I'm a newbie in neural networks. I'm trying to fit my neural network that has 3 different outputs: semantic segmentation, box mask and box coordinates. When my model is training, the loss of ...
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1answer
21 views

How do I optimize the number of filters in a convolution layer?

I’m trying to figure out how to write an optimal convolutional neural network with respect to maximizing and minimizing filters in a convolution 2D layer. This is my thinking and I’m not sure if it's ...
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Which generative methods are better for generating graphs, while preserving node and edge labels?

I started to dig into the topic of graph generation and I have a question - which out of generative methods (autoregressive, variational autoencoders, GANs, any other?) are better for generating ...
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1answer
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Are POS tagging, Chunking, Disambiguation, etc. subtasks of annotation?

I wonder about the legitimacy of using the terms "POS tagging", "Chunking", "Disambiguation" and "Categorization" to describe an activity that doesn't include writing code and database queries, or ...
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Can I predict the label of purposely unlabelled observations?

Let's say I have a data set with of length N. A small proportion N2 is labeled. Can I remove some labels and then 'reverse' this action with a trained neural network? I could then use the same process ...
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1answer
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Why does the machine learning algorithm need to learn a set of functions in the case of missing data?

I am currently studying the textbook Deep Learning by Goodfellow, Bengio, and Courville. Chapter 5.1 Learning Algorithms says the following: Classification with missing inputs: Classification ...
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How to detect vanishing gradients in tensorboard?

I have two "sub-questions" 1) How can I detect vanishing or exploding gradients with Tensorboard, given the fact that currently write_grads=True is deprecated in the Tensorboard callback as per "un-...
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In Python, how to combine 3D arrays into one 4D array? [closed]

I have N arrays of dimension (1080, 1920, 3). I wanna combine them into one array of dimension (N, 1080, 1920, 3). I code it, but not quite satisfied, could you help me?
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How can I use Keras on a mobile app? [closed]

I have a trained network for machine translation. I want to use Keras on mobile and only load the weights to the app. I found keras.js, but I don't know how to use it for a mobile app. I use ionic4 ...
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Weird border artifacts when training a CNN

I've been trying to use this DeepLabv3+ implementation with my dataset (~1000 annotated images of the same box, out of the same video sequence): https://github.com/srihari-humbarwadi/...
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Is traditional machine learning obsolete since neural nets, deep neural nets can always outperform them?

I have been coming across visualizations showing that the neural nets tend to perform better as compared to the traditional machine learning algorithms (Linear regression, Log regression, etc.) ...
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How do I poison an SVM with manifold regularization?

I'm working on Adversarial Machine Learning, and have read multiple papers on this topic, some of them are mentioned as follows: Poisoning Attacks on SVMs: https://arxiv.org/pdf/1206.6389.pdf ...
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Is there a family tree for reinforcement learning algorithms?

Can anyone point me in the direction of a nice graph that depicts the "family tree", or hierarchy, of RL algorithms (or models)? For example, it splits the learning into TD and Monte Carlo methods, ...
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Steps to train and re-train a good model

I'm still a bit new to deep learning. What I'm still struggling, is what is the best practice in re-training a good model over time? I've trained a deep model for my binary classification problem (...
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How does the BERT model (in Tensorflow or Paddle-paddle frameworks) relate to nodes of the underlying neural-net that's being trained?

The BERT model in frameworks like TensorFlow/Paddle-paddle shows various kinds of computation nodes (like subtract, accumulate, add, mult etc) in a graph like form in 12 layers. But this graph doesn'...
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How should I penalize the model proportionally to the error?

I am making an MNIST classifier. I am using categorical cross-entropy as my loss function. I want to make it so that if the correct label is 3, then it will penalize the model less heavily if it ...
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can i use machine or deep learning in windows instead of Ubuntu? [closed]

I'm new to Machine Learning and want to start to learn it .. Does windows environment be good or i must use Ubuntu
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1answer
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What does the cos (or sin) function actually do? [closed]

In the context of ML, using the example equation below, what is the cos function actually doing? It would be great if you could tell me the answer to this very simple example and how you came to the ...
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What will be the sequence of steps in a human activity recognition model using LSTM?

In the context of these steps detection, tracking, action classification and activity recognition. Which step will be first and further sequence?
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What are some of the best methods in detecting facial movement using state-of-the-art machine learning models?

I am currently working on implementing a lip reading system in Python using machine learning and image processing. Currently, two initial implementations have provided promising results, albeit not ...
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1answer
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Where can I find good tutorials on user tailored recommendation system for web?

I'm currently working on my uni project, but I have no idea where to start for the user tailored recommendation system on web. Where can I find a good guide on it, preferrably on languages like php ...
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36 views

Machine learning frameworks for esoteric languages

Is there a machine learning framework/library for any of the esoteric languages, such as the ones listed here ?
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Is it possible to convert Neural Network code in Python into Matlab code? [closed]

I want to convert the code written in Python into Matlab code. May I know is it possible to do that. Share the available ways or methods to do the conversion. May I know is there any Online ...
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1answer
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What is teacher forcing?

In the paper Neural Programmer-Interpreters, the authors use the teacher forcing technique, but what exactly is it?
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Pytorch deep learning models and tabular data representation

I have quite a naive question regarding Pytorch deep learning models and tabular data representation. So, assume I have a dictionary of tables. Each table has some number of columns: categorical and ...
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Noise Cancellation on live audio stream

I want to build an application which takes a live audio from source (mic) and filtering the noise (unwanted sounds like chattering, traffic noises) and fetch into an application for further processing....
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1answer
28 views

How does batch size affect model size?

I'm suffering from a significant brain fart while trying to get my head around how does batch size affect overall model size e.g for CNNs. Does it serve as an additional dimension for all the weight ...
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1answer
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Trained a regression network and getting EXACT same result on validation set, on every epoch

I trained this network from this github. The training went well, and returns nice results for new, unseen images. On training, the loss changed (decreased), thus I must assume the weights changed as ...
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1answer
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Which is a better form of regularization: lasso (L1) or ridge (L2)?

Given a ridge and a lasso regularizer, which one should be chosen for better performance? An intuitive graphical explanation (intersection of the elliptical contours of the loss function with the ...
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3answers
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While we split data in training and test data, why we have two pairs of each?

Why do we split the data into two parts, and then split those segments into training and testing data? Why do we have two sets of data for each training and test data?
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Does bag-of-words method improve the classification accuracy?

I'm a beginner in computer vision. I want to know which structure among the following two can get better accuracy of image classification. Structure 1: SIFT feature + SVM Structure 2: bag-of-word ...
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Neural network seems to just figure out the probability of a specific result

I am really new to neural networks, so i was following along with a video series, created by '3blue1brown' on youtube. I created an implementation of the network he explained in c++. I am attempting ...
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Architecture of the encoder in a Bi-GAN?

I know this is a subjective question, but I was thinking how does one decide on their encoder architecture in the case of Bi-directional GANs. The first idea coming to my mind is basically mirroring ...
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1answer
25 views

How to represent and work with the feature matrix for graph convolutional network (GCN) if the number of features for each node is different?

I have a question regarding features representation for graph convolutional neural network. For my case, all nodes have a different number of features, and for now, I don't really understand how ...
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17 views

Tensorflow throwing out of bounds error with keras tokenizer

I am new to ML and tensorflow and trying to train and use a standard text generation model. When I go to train the model I get this error: ...
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1answer
27 views

Why is exp used in encoder of VAE instead of using the value of standard deviation alone?

There's one VAE example here: https://towardsdatascience.com/teaching-a-variational-autoencoder-vae-to-draw-mnist-characters-978675c95776. And the source code of encoder can be found at the ...
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2answers
149 views

How to estimate the capacity of a neural network?

Is it possible to estimate the capacity of a neural network model? If so, what are the techniques involved?
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1answer
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Interpolating image to increase resolution before feeding it to a neural network

Interpolation is a common way to make an image fit the right input shape for a neural network. But is there any point in using interpolation to make it easier for the network to learn? I assume ...
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Post-classification after inference

I designed a fire detection using Deep Learning based classification approach. In my training dataset, I have both fire and fire smokes are supposed to be detected (all under "fire"; mostly real fires ...
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2answers
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Mathematical foundations of the ability to learn

I am an undergraduate student in applied mathematics with an interest in artificial intelligence. I am currently exploring topics where I could do research. Coming from a mathematical background I am ...
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1answer
23 views

What is the 'Mean' in Variational Auto-encoder

Here's an example of Variational Auto-Encoder (VAE): There are 2 nodes before the Sample (encoding vector). One is 'Mean', one is 'Standard Deviation', the 'Mean' one is confusing. Is it Mean of ...
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1answer
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Recognize carp and give them a unique id

For my internship assignment I have to implement a proof of concept for an application that is supposed to scan a picture with a carp on it and identify which carp this is. All of the carps that are ...
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1answer
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Why is Standard Deviation based on L2 Variance and not L1 Variance

Standard deviation and variance are in statistics but the formula for variance is somehow related to the L1 and L2. Mathematically (L2 in machine learning sense), $$Variance = \dfrac{(X_1-Mean)^2+..+(...
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Is Gradient Descent algorithm a part of Calculus of Variations?

As in https://en.wikipedia.org/wiki/Calculus_of_variations The calculus of variations is a field of mathematical analysis that uses variations, which are small changes in functions and ...
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2answers
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What is the name of this neural network architecture with layers that are also connected to non-neighbouring layers?

Consider a feedforward neural network. Suppose you have a layer of inputs, which is feedforward to a hidden layer, and feedforward both the input and hidden layers to an output layer. Is there a name ...
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54 views

Are PAC learnability and the no free lunch theorem contradictory?

I am reading the Understanding Machine Learning book by Shalev-Shwartz and Ben-David and based on the definitions of PAC learnability and No Free Lunch Theorem, and my understanding of them it seems ...
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What is the difference between graph semi-supervised learning and normal semi-supervised learning?

Whenever I look for papers involving semi-supervised learning, I always find papers that talk about graph semi-supervised learning. Could someone explain the difference between the two?

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