# Tag Info

Accepted

### What is "conditioning" on a feature?

This is conditioning in the sense of conditional probability. The idea is that the authors have some "standard physically-inspired features". They are splitting the data up into bins based on the ...
• 8,877
Accepted

### Does the correlation between inputs affect the model performance?

Non-correlation does not imply independence, that is, if two features are not correlated (i.e. zero correlation), it does not mean that they are independent. But (non-zero) correlation implies ...
• 33.8k

### How to design a neural network that gets the author name of a piece of art as input?

The most straightforward approach I would recommend would be the one-hot encoding solution without a feature for ''other author''. If you use drop-out during training, the network should learn how to ...
• 9,379

### How to convert color information to 1D feature vector?

My 2 suggestions would be to: Sum the hex coding of the color multiplied by the prevalence. For example [80, 80, 80] (grey) is used 7% of the time so ...
• 175
Accepted

### What are bag-of-features in computer vision?

Introduction Bag-of-features (BoF) (also known as bag-of-visual-words) is a method to represent the features of images (i.e. a feature extraction/generation/representation algorithm). BoF is inspired ...
• 33.8k
Accepted

### How to add more features to the input of a machine learning algorithm?

Data pre-processing and feature extraction are by far the most important part of any machine learning algorithm. It's even more important that the model you choose to do the classification. ...
• 450

### How to design a neural network that gets the author name of a piece of art as input?

I would try to find some proxy features about the author, as opposed to encode the identity of the author. Likely good features of an author include averages of other features about the work (such as ...
• 23.8k
Accepted

### What is the difference between features and inputs in machine learning?

An input usually refers to an example (sometimes also known as sample, observation or data point) $x$ from a dataset that you pass to the model. For example, in supervised learning, you have a ...
• 33.8k

### How to convert color information to 1D feature vector?

I think the tool you found is useful for a human and to get nice visuals but I also think it's totally useless for feature extraction. If you want to pass explicit information about colors simply ...
• 3,513
Accepted

### How to handle varying types and length of inputs in a feedforward neural network?

In short ANNs don't have problems with "different types" of data as long as they are represented using real numbers: the inputs for your ANN represent lengths and are easy to understand and ...
• 361
Accepted

### What is a temporal feature?

In general, the expression "temporal feature" might refer to any feature that is associated with or changes over time. However, in the context of signal processing, a temporal feature might ...
• 33.8k
Accepted

### How do neural networks weigh multiple inputs/features of different dimensionality?

As stated in your example, the three features are: an image, a price, a rating. Now, you want to build a model that uses all of these features and the simplest way to do is to feed them directly into ...
• 71

### Do the eigenvectors represent the original features?

The principal components (eigenvectors) correspond to the direction (in the original n-dimensional space) with the greatest variance in the data. The corresponding eigenvalue is a number that ...
• 121
Accepted

### How do we know that the neurons of an artificial neural network start by learning small features?

We do it experimentally; you're able to look at what each layer is learning by tweaking various values throughout the network and doing gradient ascent. For more detail, watch this lecture: https://...
• 1,344
Accepted

### Does feature scaling have any benefits if all features are on the same scale?

If all you features are binary, then, you don't need to apply normalization on them. Since their values are on the same scale already.

### How are small scale features represented in an Inverse Graphics Network (autoencoder)?

Simply said, there is no specific "meaning" to the features generated. They are simply features that are fitted through math and calculus, and nobody knows what they represent exactly, and will never ...
• 1,715
Accepted

### Can I always interpret features as random variables in machine learning safely?

In general terms yes. Because what the ML algorithms do in general is to learn the hidden probability density function of the target examples (cats, dogs..). And that is done by learning the ...
• 1,018
Accepted

### When is it necessary to manually extract features to feed into the neural network rather than providing raw data?

Yes, neural networks learn features themselves freeing you from the need to manually engineer them. I will illustrate it here with a toy problem. Let's assume that we want to learn the areas of ...

### When is it necessary to manually extract features to feed into the neural network rather than providing raw data?

Feature engineering may be necessary when one cannot achieve acceptable error rate — within a budget or in principle. NN may be stalling due to information bottleneck: too many pigeons, not enough ...

### Is it true that channels always represent colours of an image?

No, channels do not have to only represent colours. It is common for them to represent other things, even without considering feature maps. For instance RGBD images, where D is a depth measurement or ...
• 23.8k
1 vote
Accepted

### How to find "relationships" between two data representations?

Well, I suppose one can use some kind of contrastive learning in this case. A famous example of the establishment of relation between two different representations ...
1 vote

### Can we train the model to detect real users with only positive labels?

Pragmatically you could use the discriminatory from a GAN for outlier detection. Ideally you'd start collecting fakes now and do a normal model on both good and bad cases. In the absence of that you ...
• 880
1 vote

### Can we train the model to detect real users with only positive labels?

The problem which you have is a classification problem. You assume a class "good users" and a distinct class "bad users". You want to train an AI to tell the two apart, but all your examples are "good ...
• 121
1 vote

### How do we know that the neurons of an artificial neural network start by learning small features?

The network architecture is relevant to this question. Convolutional neural network architectures enforce the building up of features because the neurons in earlier layers have access to a small ...
• 83
1 vote

### Does the weight vector form imply feature space curvature?

No, it does not take into account the curvature. But, if curvature is important for you, then, it would be a good idea to look at Ricci flow and its applications in neural networks.
1 vote

### How to predict the best from a set of messages - best practice

One way you can definitely approach the problem is by using (Deep) Reinforcement Learning (DRL). YouTube is actually using DRL as well to suggest videos to users in order to maximize users' engagement ...
• 705
1 vote

### Is it possible to flip the features and labels after training a model?

The body of your post seems to be asking a completely separate question than the title of your post, so I will answer both: "Body: How do I complete the goal of this program?" Your dataset ...
• 1,230
1 vote
Accepted

### When is adding a feature useless?

Now I want to check if I can predict B directly from A, since, in my understanding, this would mean that info on B is already inside A. This will help inform you how much redundancy there is between ...
• 23.8k
1 vote
Accepted

### What is the correct name for state explosion from sensor discretization?

I would refer to your problem as having a continuous state space. By using a 32-bit float variable you discritize it. However, creating states for every possible value of a 32-bit float variable is ...
• 365

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