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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3answers
290 views

Are there any rules of thumb for having some idea of what capacity a NN model needs to have for a given problem?

To give an example. Let's just consider the MNIST dataset of handwritten digits. Here are some things which might have an impact on the optimum model capacity: There are 10 output classes The inputs ...
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0answers
21 views

Feed data into Keras LSTM layer [closed]

I'm trying to understand how to feed data into LSTM layer of Keras, but I'm in trouble and I don't understand how to do it. I've ...
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0answers
45 views

Should I restart doing research in artificial intelligence, after 20 years of being away from this field? [closed]

I am seeking some advice. I worked on Artificial Intelligence, Machine Learning, Neural Networks back in the 1990s, published papers, built prototypes all in academia. When I joined the workforce ...
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0answers
59 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 ...
0
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0answers
14 views

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 ...
3
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1answer
69 views

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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0answers
13 views

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 ...
1
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1answer
48 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 ...
1
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0answers
20 views

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
18 views

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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0answers
30 views

Semi-supervised: 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
40 views

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 ...
2
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1answer
62 views

How to detect vanishing gradients?

Edit: I've reworked my question to generalize better and be more on-topic, and be mostly software implementation agnostic. Can vanishing gradients be detected by the change in distribution (or lack ...
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0answers
30 views

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/...
2
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1answer
46 views

Is traditional machine learning obsolete given that neural networks typically 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.) ...
3
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1answer
90 views

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 ...
3
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1answer
93 views

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, ...
1
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0answers
21 views

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 (...
2
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1answer
30 views

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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1answer
52 views

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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0answers
10 views

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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0answers
20 views

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 ...
1
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0answers
19 views

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 ...
2
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0answers
45 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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0answers
22 views

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 ...
1
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1answer
66 views

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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0answers
20 views

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 ...
1
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0answers
24 views

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....
1
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1answer
39 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 ...
1
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1answer
17 views

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 ...
3
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1answer
61 views

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 ...
1
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3answers
49 views

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?
2
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1answer
21 views

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 ...
1
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0answers
19 views

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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0answers
8 views

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 ...
3
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2answers
51 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 ...
1
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1answer
31 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 ...
4
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2answers
222 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?
1
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1answer
24 views

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 ...
1
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0answers
16 views

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 ...
4
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2answers
115 views

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 ...
2
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1answer
35 views

What is the mean in the variational auto-encoder?

Here's a diagram of a variational auto-encoder. There are 2 nodes before the sample (encoding vector). One is the mean, one is the standard deviation. The mean one is confusing. Is it the mean of ...
2
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1answer
31 views

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 ...
1
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1answer
39 views

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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0answers
31 views

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 ...
2
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2answers
71 views

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 ...
4
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2answers
119 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 ...
2
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1answer
31 views

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 some that talk about graph semi-supervised learning (e.g. A Unified Framework for Data Poisoning Attack to Graph-based Semi-...
2
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0answers
16 views

What effect does increasing the actions in RL have?

Consider a 2D snake game, where the snake has to eat food to become longer. It must avoid hitting walls and biting into her tail. Such a game could have a different amount of actions: 3 actions: go ...
3
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0answers
34 views

In machine learning, how can we overcome the restrictive nature of conjunctive space?

In machine learning, problem space can be represented through concept space, instance space version space and hypothesis space. These problem spaces used the conjunctive space and are very restrictive ...

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