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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2answers
958 views

Are information processing rules from Gestalt psychology still used in computer vision today?

Decades ago there were and are books in machine vision, which by implementing various information processing rules from gestalt psychology, got impressive results with little code or special hardware ...
9
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1answer
102 views

Will parameter sweeping on one split of data followed by cross validation discover the right hyperparameters?

Let's call our dataset splits train/test/evaluate. We're in a situation where we require months of data so we prefer to use the evaluation dataset as infrequently as possible to avoid polluting our ...
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5answers
7k views

Which machine learning algorithm can be used to identify patterns in a dataset of the cache performance of a CPU?

I need a machine learning algorithm to identify patterns in a dataset (saved in a CSV file) that contains details of the cache performance of a CPU. More specifically, the dataset contains columns ...
6
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2answers
227 views

Back-of-the-envelope machine learning (specifically neural networks) calculations

There is a popular story regarding the back-of-the-envelope calculation performed by a British physicist named G. I. Taylor. He used dimensional analysis to estimate the power released by the ...
5
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0answers
51 views

Question about minimizing sum of remainders

I have a set of integers [$c_1$, $c_2$, $c_3$, ... , $c_N$]. A non-negative integer D, greater than a certain threshold, divides each 𝑐𝑖 and leaves remainder π‘Ÿπ‘–,i.e., $r_i$ can be written as $r_i=...
5
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1answer
33 views

Video summarization similar to Summe's TextRank

We have the popular TextRank API which given a text, ranks keywords and can apply summarization given a predefined text length. I am wondering if there is a similar tool for video summarization. ...
5
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1answer
88 views

How to deal with small amount of labeled samples?

I'm trying to develop skill to deal with very small amount of labeled samples (250 labeled/20000 total, 200 features) practicing on Kaggle "Don't Overfit" dataset (Traget_Practice have provided all 20,...
5
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1answer
110 views

How to detect frauds in advertising business using machine learning?

I am very beginner to this world. I still learning the basics of Machine learning and AI but i have a problem at hand and i am not sure which technique or Algorithm can be applied on it. I am working ...
5
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0answers
55 views

Choosing Machine Learning Algorithm: Learning-Based Testing

This is my first project using machine learning so I'm looking for some guidance. I am extending a model-based testing (MBT) system to a learning-based testing system by integrating a machine learning ...
5
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2answers
799 views

Is it possible to use AI to reverse engineer software?

I was thinking of something of the sort: Build a program (call this one fake user) that generates lots and lots and lots of data based on the usage of another program (call this one target) using ...
5
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1answer
100 views

Which Rosenblatt's paper describes Rosenblatt's perceptron training algorithm?

I struggle to find Rosenblatt's perceptron training algorithm in any of his publications from 1957 - 1961, namely: Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms The ...
5
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1answer
212 views

Other Deep Learning Networks for Visual Place Recognition?

I am doing a project on Visual Place Recognition in Changing Environments. The CNN used here is mostly AlexNet, and a feature vector is constructed from Layer 3. Does anyone know of similar work ...
4
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0answers
64 views

Is there a mathematical formula that describes the learning curve in neural networks?

In training a neural network, you often see the curve showing how fast the neural network is getting better. It usually grows very fast then slows down to almost horizontal. Is there a mathematical ...
4
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0answers
50 views

Why would the application of boosting prevent underfitting

"Why would the application of boosting prevent underfitting" I read in some paper that applying boosting would prevent you from underfitting. Why is that? Source: http://www.cs.cornell.edu/courses/...
4
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1answer
65 views

How does the network know which objects to track in the paper “Label-Free Supervision of Neural Networks with Physics and Domain Knowledge”?

I was reading the paper Label-Free Supervision of Neural Networks with Physics and Domain Knowledge, published at AAAI 2017, which won the best paper award. I understand the math and it makes sense. ...
4
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0answers
49 views

Is it possible to control asymptotic behaviour of neural network models?

Is it possible to specify what the asymptotic behaviour of a Neural Networks (NN) model should be? I am thinking on NN which try to learn a mapping $\vec y=f(\vec x)$ with $\vec x$ a vector of ...
4
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1answer
139 views

An intuitive explanation of Adagrad, its purpose and its formula

It (Adagrad) adapts the learning rate to the parameters, performing smaller updates (i.e. low learning rates) for parameters associated with frequently occurring features, and larger updates (i.e. ...
4
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1answer
85 views

Backpropagation equation for a variant on the usual Linear Neuron architecture

Recently I encountered a variant on the normal linear neural layer architecture: Instead of $Z = XW + B$, we now have $Z = (X-A)W + B$. So we have a 'pre-bias' $A$ that affects the activation of the ...
4
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1answer
60 views

How are mean and variance calculated in Bayesian curve fitting?

This page is from Pattern Recognition by Bishop. What is the explanation for equations 1.70. 1.71 and 1.72? Edit: The distributions are all Gaussian.
4
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1answer
89 views

Will BERT embedding be always same for a given document when used as a feature extractor

When we use BERT embeddings for a classification task, would we get different embeddings every time we pass the same text through the BERT architecture? If yes, is it the right way to use the ...
4
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1answer
92 views

How do big companies, like Facebook, model individuals and their interaction?

As a layman in AI, I want to get an idea of how big data players, like Facebook, model individuals (of which they have so many data). There are two scenarios I can imagine: Neural networks build ...
4
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0answers
110 views

Is there research about teaching AI to “analyze the problem and design a solution”?

Update on 2019-05-19: My question is about teaching AI to solve the problem, not letting AI teach a human developer to solve a problem. Original post: I'm a software developer but very new to AI. ...
4
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0answers
56 views

How do the relative number of cells between neighboring stacked LSTM layers affect the network's behavior?

It seems that stacking LSTM layers can be beneficial for some problem settings in order to learn higher levels of abstraction of temporal relationships in the data. There is already some discussion on ...
4
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0answers
46 views

What is the motivation for row-wise convolution and folding in Kalchbrenner et al. (2014)?

I was reading the paper by Kalchbrenner et al. titled A Convolutional Neural Network for Modelling Sentences and I am struggling to understand their definition of convolutional layer. First, let's ...
4
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1answer
224 views

Point A to B Avoidance

I understand A* and Dijkstra for avoiding obstacles, they require that points are traversable there are points that are not traversable thus the algorithms wont bump into the obstacles because the ...
4
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0answers
483 views

Sparsity constraint in a deep autoencoder

Is there any way and any reason why one would introduce a sparsity constraint on a deep autoencoder? In particular, in deep autoencoders the first layer often has more units than the dimensionality ...
4
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2answers
2k views

What is the purpose of “reshaping it into the shape the network expects and scaling it so that all values are in the [0, 1] interval.”?

I am a deep learning beginner recently reading this book "Deep learning with Python", the example explains the process of implementing a greyscale image classification using MNIST in keras, in the ...
4
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0answers
112 views

Supervised K-means clustering doesn't appear to work

I have a data set containing actions taken by customers (e.g., view a product, add a product to cart, purchase product), the product bought (if any) and times of said actions. I am attempting to use K-...
4
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0answers
155 views

How to feed a variable size sequences into a CNN?

If I want to train a convoluted NN on time series but I cannot decide where to split the data. I see that other people use jumping window over the input. so the feed say 20 sec of observation as 1 ...
4
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1answer
751 views

Traveling salesman problem variant: which algorithm to choose?

I have an industrial problem which I'm trying to cast as a Traveling Salesman problem (TSP) in 3D euclidian space. There are physical limitations which implies that some subpaths may or may not be ...
3
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0answers
35 views

Which machine learning method can take a matrix as input?

I am pretty new to the machine learning field. I want to use an $n \times m$ matrix as the input of a model, in order to predict a vector $1 \times m$, both of real numbers. Input data are quite clean,...
3
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1answer
25 views

What should the dimension of the input be for text summarization?

I am trying to build a model for extractive text summarization using keras sequential layers. I am having a hard time trying to understand how to input my x data. Should it be an array of documents ...
3
votes
1answer
87 views

What are the differences between artificial neural networks and other function approximators?

Modern artificial neural networks use a lot more functions than just the classic sigmoid, to the point I'm having a hard time really seeing what classifies something as a "neural network" over other ...
3
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0answers
26 views

Could zero-padding affect learning in a negative way?

I implemented an LSTM with Keras to perform word ordering task (given a syntactically unordered sentence, the goal is to label ...
3
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0answers
32 views

Is maximum likelihood estimation meaningless for a dataset of only outliers?

From my understanding, maximum likelihood estimation chooses the set of parameters for the estimator that maximizes likelihood with the ground truth distribution. I always interpreted it as the ...
3
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0answers
46 views

How should I select the features for predicting diseases?

My aim is to create a trained model for predicting diseases. Now diseases are classified based on the following criteria in general: Causes of the Disease Pathogenesis (the mechanism by which the ...
3
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0answers
35 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 ...
3
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0answers
27 views

Rarely predict minority class imbalanced datasets

I have a dataset in which class A has 99.8%, class B 0.1% and class C 0.1%. If I train my model on this dataset, it predicts always class A. If I do oversampling, it predicts the classes evenly. I ...
3
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0answers
50 views

DQN, how to choose the reward fucntion?

I built a simple AI system that tries to solve the 8 puzzle using DQN. The problem is, if the agent gets only a reward greater than zero when winning, the training will take a long time, so I made a ...
3
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2answers
49 views

Are there any easy ways to create annotated training images for object detection?

For the purposes of object detection, are there any easy ways to create annotated training images? For example, if we have $10,000$ images and want to draw bounding boxes on 2 objects for each image, ...
3
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0answers
40 views

Why do Bayesian algorithms work well with small datasets?

I read very often that Bayesian algorithms work well on small datasets. Why is that? I think it is because they might generalize more, but why is that? See also Investigating the use of Bayesian ...
3
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0answers
23 views

What Are the key Plugins And Features to implement in AI Assistant Personal Project?

Hope you're doing great I'm doing a personal project for android, I want to make an app in Android Studio. The App will be an AI Assistant Like Siri, Jarvis , etc. The idea in mind is to make the app ...
3
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0answers
17 views

Which hyper-parameters are considered in neural architecture search?

I want to understand automatic Neural Architecture Search (NAS). I read already multiple papers, but I cannot figure out what the actual search space of NAS is / how are classical hyper-parameters ...
3
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0answers
72 views

Are there datasets to solve differential equations in a supervised fashion?

Are there datasets to solve differential equations in a supervised fashion? More precisely, the input is a differential equation and the label should be the general solution to that differential ...
3
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0answers
51 views

Vector normalization by a neural network

I'm wondering if there is a NN that can achieve the following task: Output a unit vector that is parallel to the input vector. i.e., input a vector $\mathbf{v}\in\mathbb{R}^d$, output $\mathbf{v}/\|\...
3
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0answers
39 views

What is the difference between random and sequential sampling from the reply memory?

I was working on an RL problem and I am confused at one specific point. We use replay memory so that the network learns about previous actions and how these actions lead to a success or a failure. ...
3
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0answers
47 views

When to use RMSE as opposed to MSE and vice versa?

I understand that RMSE is just the square root of MSE. Generally, as far as I have seen, people seem to use MSE as a loss function and RMSE for evaluation purposes, since it exactly gives you the ...
3
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0answers
17 views

Reverse engineering controller sensitivity/aim for several games ie acceleration curves, deadzones, etc

A machine learning project I am working on requires me to interface with an Xbox controller connected to a PC. The implementation must do the following two things: Record the joystick input from the ...
3
votes
0answers
62 views

What are the differences between CRF and HMM?

What I know about CRF is that they are discriminative models, while HMM are generative models, but, in the inference method, both use the same algorithm, that is, the Viterbi algorithm, and forward ...
3
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0answers
34 views

Where could I find information on the learning methods used in Neurogrid?

I have been searching for more than one week which learning methods were used in Neurogrid. But I only found descriptions of its architecture (chips, circuits, analog and/or digital components, ...

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