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How to properly name given type of classification problem?

This is called multi-task learning, as you have two independent classification tasks. Note that this is not the same as multi-label learning.
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1 vote

Using GraphSAGE model for multigraph datasets

My question is if GraphSAGE is suitable for this kind of data? To my knowledge, GraphSAGE is designed for very large graphs with highly connected nodes (like social networks). The neighborhood ...
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1 vote

How to properly name given type of classification problem?

I think what you are looking for is multi-label classification. Multi-label classification can take care of both of your constraints - each data sample can be classified according to two different ...
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1 vote
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Why are SVMs / Softmax classifiers considered linear while neural networks are non-linear?

I was confused because the images look similar even though in reality the problems the 2 images are solving are completely different: The first image shows a linear classifier assigning scores for ...
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1 vote

Compare Strings composed from 2-3 words using NLP/ML(Python)

You can use a model to create rich embeddings for example: sentence transformers and then use cosine similarity distance from sklearn with a threshold (at least 0.6) to create clusters of semantically-...
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2 votes
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How does Bishop derive $\ln p\left(\mathbf{x} \mid \mu, \sigma^{2}\right)$, when $p$ is a Gaussian?

This is not so difficult (just a bit verbose if you do all steps). Just replace $\mathcal{N}\left(x_{n} \mid \mu, \sigma^{2}\right)$ with $\frac{1}{\left(2 \pi \sigma^{2}\right)^{1 / 2}} \exp \left\{-\...
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What kind of NN I need to find ideal ranges and correlation between them?

What you described seems like a pretty standard binary classification problem. There are many good algorithms, that are much simpler and more interpretable than NNs. I don't see why you would straight ...
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1 vote
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How to deal with changing rewards in Q-learning? DQN?

Is my definition of 'state' or 'action' wrong? I hesitate to say 'wrong', but that's not how state and action are defined in RL, and that mismatch might make the algorithms hard to understand. In RL ...
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3 votes

Is there a way to easily simulate video games, without actually rendering the pixels on screen?

Bypassing graphics As mentioned in Neil Slater's answer, manipulating the engine to bypass graphics rendering to speed up AI simulation can be a valid approach. I have done that myself. But there are ...
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4 votes
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In Value Iteration, why can we initialize the value function arbitrarily?

If the value function of a state $v(s)$ is relatively high, then you are absolutely correct in saying that a greedy policy may choose to visit $s$, since the high $v(s)$ makes it very promising. The ...
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6 votes

In Value Iteration, why can we initialize the value function arbitrarily?

Is this something to do with the Bellman optimality constraint itself? That is part of it, and important for episodic problems without discounting. The Bellman equations link between time steps, ...
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How can I generalize a machine learning model to multiple curves?

This looks like a 1-to-many problem. Given a single scalar x value, you want your output to be an array of size N where N is the number of curves. as an example you can build a model in this way using ...
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1 vote

How it is possible to compress audio with the image representation of an audio?

The mel spectrogram is numbers. It’s not light, not photons. The plot is in the image, but the essence of the information is numbers. One of the implications of compression is that there’s a way of ...
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7 votes

Is there a way to easily simulate video games, without actually rendering the pixels on screen?

It depends on the game environment and on the model being trained. If you are training an agent that uses vision to decide action, then typically you need a copy of the rendered screen: If that ...
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1 vote

Is there any way to train a regression model with negative values that is more stable?

A couple things you could try: You could try normalizing your target variable, so that it's number of standard deviations from the mean, or mapped to [-1,1]. If you are using drop-out during training. ...
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Why do we use the same parameters for the joint, marginal and conditional distributions in VAEs?

I think this is very confusing to many people. I had to deal with VAEs (and Bayesian neural networks) multiple times in the past, and I've seen so many inconsistent notations and unclear explanations. ...
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1 vote
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Do I need to create one or many neural networks to play Risk?

What a fantastic problem! Also, welcome to AI. The challenge, and it isn't terrible, is that you have to build an NN that can ingest the problem. How do you pose the problem to the learner? Here is ...
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YearPrediction dataset for a regression task: is it possible to evaluate a fair comparison between standard loss and a quadratic one?

The way to evaluate any supervised learning result is to pick a metric - a scoring system for the results. Ideally this metric captures key details of what properties you care about for the trained ...
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ML algorithm suggestion for databases that change a lot with time after model training

It sounds to me that while the data is changing every week, it is still in the same domain. That should make things easier. You need a neural network that generalises well. Faster RCNN with ResNet as ...
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