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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1answer
73 views

Applications of AI for creatives and artists

I have just watched a few videos on Tedtalks talking about how AI benefits creatives and artists but none of the videos I watched provided further resources for reference. So far, I have only came ...
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1answer
91 views

Why do we need multiple LSTM units in a layer?

What is the point of having multiple LSTM units in a single layer? Surely if we have a single unit it should be able to capture (remember) all the data anyway and using more units in the same layer ...
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0answers
40 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 ...
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2answers
130 views

What does it mean for a neuron in a neural network to be activated?

I just stumbled upon the concept of neuron coverage, which is the ratio of activated neurons and total neurons in a neural network. But what does it mean for a neuron to be "activated"? I know what ...
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3answers
125 views

How can I develop a prediction algorithm for a game of chance?

How can I develop a prediction algorithm in the case of games of chance? Suppose there is a 50:50 chance of winning. Is there way of creating a prediction algorithm?
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0answers
24 views

Showing machine learning results to Group CEO

I am working as a Data Scientist in a non IT company(in fortune 500) and the group CEO is visiting the Data Science department after it's inception a few months back. We have models like chrun ...
2
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2answers
76 views

Why is MSE used over other quadratic loss functions?

So I was wondering, why I have only encountered square loss function also known as MSE. The only nice property of MSE I am so far aware of is its convex nature. But then all equations of the form $x^{...
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1answer
22 views

Is this learning rate schedule increasing the learning rate?

I was reading a PyTorch code then I saw this learning rate scheduler: ...
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3answers
98 views

Is machine learning required for deep learning?

The answers to this Quora question say it's OK to ignore machine learning and start right away with deep learning. Is machine learning required or is useful for understanding (theoretically and ...
2
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3answers
221 views

Are expert systems dead?

Besides all the fashion about machine learning, data analysis and reinforcement learning, what is going on in the expert systems field and symbolic AI ? There are plenty of domains where machine ...
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9answers
5k views

Is artificial intelligence vulnerable to hacking?

The paper The Limitations of Deep Learning in Adversarial Settings explores how neural networks might be corrupted by an attacker who can manipulate the data set that the neural network trains with. ...
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1answer
20 views

Understanding the equation of the empirical error

The empirical error equation given in the book Understanding Machine Learning: From Theory to Algorithms is My intuition for this equation is: total wrong predictions divided by the total number of ...
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1answer
41 views

What will change when workstations will have ARM Machine Learning Processors onboard?

lately we read that many manufacturers are forcing ARM architectures to be used on future workstations. One of ARM's recent announcements is a machine learning processor. What will change in terms of ...
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1answer
19 views

Prove that in such cases, it is possible to find an ERM hypothesis for $H_n$ in the unrealizable case in time $O(mnm^{O(n)})$

Let $H_1$ , $H_2$ ,... be a sequence of hypothesis classes for binary classification. Assume that there is a learning algorithm that implements the ERM rule in the realizable case such that the ...
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0answers
7 views

Looking to use AttnGAN and train it myself using my own images. Is this possible? How difficult would this be for myself to learn?

I am an artist and I am interested in training an image generating AI, specifically AttnGAN, using my own images. I would also consider hiring someone to set this up then show me how to use it, but I’...
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0answers
50 views

How to develop face recongiton program using CNN to obtain more than 95% accuracy? [on hold]

I want to develop face recognition program using convolutional neural network. Can some one tell me steps to follow to do the same? I am new to deep learning. I want to develop it on windows using ...
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0answers
7 views

How does keras `train_on_batch` return value work?

From the doc, train_on_batch() will return a scalar representing the loss and the metric. I want to know whether the loss/metric is evaluated before the weight is ...
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1answer
132 views

Which machine learning models are universal function approximators?

The universal approximation theorem states that a feed-forward neural network with a single hidden layer containing a finite number of neurons can approximate a wide variety of interesting (...
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1answer
40 views

“Outside-in” versus “Inside-out” machine learning

A little background... I’ve been on-and-off learning about data science for around a year or so, however, I started thinking about artificial intelligence a few years ago. I have a cursory ...
5
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1answer
123 views

What is a weighted average in a non-stationary k-armed bandit problem?

In the book Reinforcement Learning: An Introduction (page 25), by Richard S. Sutton and Andrew G. Barto, there is a discussion of the k-armed bandit problem, where the expected reward from the bandits ...
2
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2answers
76 views

Which neuron represents which part of the input?

In a neural network, each neuron represents some part of the input. For example, in the case of a MNIST digit, consider the stem of the number 9. Each neuron in the NN represents some part of this ...
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0answers
5 views

Why word embedding such as word2vec is not used as the output layer of a seq2seq decoder?

It would make sense to make the decoder predict a smaller embedding vector instead of softmax over a large dictionary. The word having the most cosine similarity with the output embedding could be ...
8
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1answer
147 views

What are the implications of the “No Free Lunch” theorem for machine learning?

The No Free Lunch (NFL) theorem states (see the paper Coevolutionary Free Lunches by David H. Wolpert and William G. Macready) any two algorithms are equivalent when their performance is averaged ...
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0answers
32 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 𝑟𝑖,i.e., $r_i$ can be written as $r_i=...
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1answer
37 views

What is the difference between the definition of a stationary policy in reinforcement learning and contextual bandit?

A stationary policy is a function that maps a state to a probability distribution of actions. In a contextual bandit problem, a state itself does not include the history. But in a reinforcement ...
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2answers
82 views

How much can the addition of new features improve the performance?

How much can the addition of new features improve the performance of the model during the optimization process? Let's say I have a total of 10 features. Suppose I start the optimisation process using ...
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0answers
14 views

How to recognize two different objects with the similar shape, but different size

I am using Mask-RCNN neural network. I retrained my network to detect and mask wheels of die-cast toy cars. I am using images, which present the side of the car (left or right). Sometimes the cars ...
2
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1answer
49 views

Markov property in maze solving problem in reinforcement learning

By definition, every state in RL has Markov property, which means that the future state depends only on the current state, not the past states. However, I saw that in some case we can define a state ...
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0answers
19 views

What are the examples of agents that is represent these characteristics?

I'm looking for examples of AI systems or agents that best represent these five characteristics (one example for each characteristics): Reactivity Proactivity Adaptability Sociability Autonomy It ...
2
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2answers
92 views

How can I determine the mathematical relation between the input and output variables?

I would like to take in some input values for $n$ variables, say $R$, $B$, and $G$. Let $Y$ denote the response variable of these $n$ inputs (in this example, we have $3$ inputs). Other than these, I ...
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1answer
26 views

Why PCA works well while the total variance retained is small?

I'm learning machine learning by looking through other people's kernel on kaggle, specifically this Mushroom Classification kernel. The author first applyed PCA to the transformed indicator matrix. He ...
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3answers
84 views

Is there a way of pre-determining whether a CNN model will perform better than another?

I developed a CNN for image analysis. I've around 100K labeled images. I'm getting a accuracy around 85% and a validation accuracy around 82%, so it looks like the model generalize better than ...
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1answer
19 views

How can I cluster based on the complementary categories?

K-means tries to find centroid and then clusters around the centroids. But what if we want to cluster based on the complement? For example, suppose we have a group of animals and we want to cluster ...
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2answers
154 views

Handling emotion in informal text (Hi vs HIIIIII!!!!)?

This is a question related to Neural network to detect "spam"?. I'm wondering how it would be possible to handle the emotion conveyed in text. In informal writing, especially among a ...
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1answer
175 views

Why do we need both encoder and decoder in sequence to sequence prediction?

Why do we need both encoder and decoder in sequence to sequence prediction? We could just have a single RNN that, given input $x$, outputs some value $y(t)$ and hidden state $h(t)$. Next, given $h(t)$...
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0answers
14 views

Intelligent reflecting surface

I wanted to know about Intelligent reflecting surface (IRS) technology. what is the application of IRS in wireless communication? what are the competitive advantages over existing technologies?
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5answers
18k views

Why does C++ seem less widely used in AI?

I just want to know why do Machine Learning engineers and AI programmers use languages like python to perform AI task and not C++ even though C++ is technically a more powerful language than python.
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3answers
17k views

Why is Lisp such a good language for AI?

I've heard before from computer scientists and from researchers in the area of AI that that Lisp is a good language for research and development in artificial intelligence. Does this still apply, with ...
9
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4answers
229 views

What are the domains where SVMs are still state-of-the-art?

It seems that deep neural networks and other neural network based models are dominating many current areas like computer vision, object classification, reinforcement learning, etc. Are there domains ...
10
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1answer
823 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 ...
7
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1answer
437 views

Loss function for Hierarchical Multi-label classification

I am looking to try different loss functions for a hierarchical multi-label classification problem. So far, I have been training different models or submodels like multilayer perceptron ( MLP )branch ...
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3answers
116 views

Feature Selection algorithm for a high featured data

I have a cancer patient database from mass spectrometry on patients which consists of more than half million features. My task is to apply a feature selection algorithm to extract the most relevant ...
4
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1answer
43 views

How to deal with large (or NaN) neural network's weights?

My weights go from being between 0 and 1 at initialisation to exploding into the tens of thousands in the next iteration. In the 3rd iteration they become so large that only arrays of nan values are ...
4
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1answer
43 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. ...
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1answer
36 views

Do we train a logistic regression model using a dataset that is 3 times bigger than the validation dataset?

Suppose we have a data set $X$ that is split as $X_{\text{train}}$, $X_{\text{val}}$ and $X_{\text{test}}$ and the outcome variable is binary. Let's say we train three different models (logistic ...
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0answers
58 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 ...
11
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1answer
175 views

What are all the different kinds of neural networks used for?

I found the following neural network cheat sheet (Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data). What are all these different kinds of neural networks used ...
3
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0answers
22 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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2answers
323 views

What is difference between edge computing and federated learning?

I recently read about federated learning introduced by Google, but it seems to be like edge computing. What is the difference between edge computing and federated learning?
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0answers
48 views

Is My Formula For a Neural Network correct?

I am creating a multi-layer neuron library in C# and doubtful of my understanding of neural network correctness as my answer even after the training of xor is always nearer 0.5. Here are the notation ...