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Apart from Reinforcement Learning, are there any other machine learning approaches to play video games?

As I see it, it all comes down to game theory, which can be said to form the foundation of successful decision making, and is particularly useful in a context, such as computing, where all parameters ...
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How can I use neural networks for detecting TV channel logos in video frames?

To perform image recognition you have to find a way to represent an image with certain features. One of the defining characteristics of a good image recognition algorithm are it's ability to detect ...
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Is there any existing attempt to create a deep learning model which extracts vector paths from bitmaps?

If we seek proven working source code to plug into a GPLv2-licence compatible solution, we should at least consider autotrace. Its source code is open for review. It can be tested against the example ...
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Is there any research on models that make predictions by also taking into account the previous predictions?

What you're describing is called a recurrent neural network. There are a large number of designs in this family that all have the ability to remember recent inputs and use them in the processing of ...
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What are the approaches to predict sequence of $\pi$ numbers?

Pseudo-random number generators are specifically defined to defeat any form of prediction via 'black box' observation. Certainly, some (e.g. linear congruential) have weaknesses, but you are unlikely ...
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How can I use neural networks for detecting TV channel logos in video frames?

Because it is video input and the logos are usually stationary because they are layered over the live or recorded frames by either hardware or software, the task is not difficult. Logos also usually ...
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What are some information processing models besides MLPs?

Neural Network equivalents that is not (vanilla) feed forward Neural Nets: Neural net structures such as Recurrent Neural Nets (RNNs) and Convolutional Neural Nets (CNNs), and different architectures ...
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Which machine learning approach should I use to estimate how many products a research group should have to improve its category?

I believe you want a neural network that can predict future values of multiple variables given multiple inputs. This belongs to the general time series forecasting problem. One of the best neural ...
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Which neural network to use for optical mark recognition?

From what I understand, don't bother with a CNN, you have essentially perfectly structured images. You can hand code detectors to measure how much filled in a circle is. Basically do template ...
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Which neural network can count the number of objects in an image?

If you want to count the number of objects using a neural network, you can use pretrained YOLO with the bottom prediction layer removed, and feed the features to a classification feed forward layer of ...
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Is there a neural network in the literature that predicts the next game state based on the current state and the action?

This is a whole sub-field of reinforcement learning known as model-based reinforcement learning. The idea in model based RL is to learn the mapping from current state/action to next state in order to ...
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Neural networks for sports betting

This is probably not going to work well as a way to make money. People with far larger budgets, and far more training, are already milking out any money to be made this way. This is probably their day ...
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Which neural network can approximate the function $y = x^2 + b$?

$f(x) = x^2 + b$ is a polynomial (more precisely, a parabola) so it is continuous, thus, a neural network (with at least one hidden layer) should be able to approximate that function (given the ...
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How many ways are there to perform image segmentation?

Apart from the multitudes of traditional image segmentation techniques (Watershed, Clustering or Variational methods), newer Segmentation schemes using Deep Learning are actively being used, which ...
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How to train an ML model to convert the given lyrics into a song by a particular singer?

OpenAI used a modified version of VQ-VAE-2 combined with sparse transformers to do something similar to what you want to do. Their approach, called Jukebox, is able to produce music by conditioning on ...
• 37k
1 vote
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What algorithms are used in Artificial General Intelligence research?

No one has ever invented a practical AGI. However, there are different approaches to the creation of AGI: universalist (e.g. AIXI) symbolic sub-symbolic (e.g. neural networks) hybrid See also this ...
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1 vote

What algorithms are used in Artificial General Intelligence research?

Current AGI approaches are very heterogenous and therefore there are no dominant algorithms. Nevertheless, I would suggest you to have a look at Knowledge Graphs and the related algorithms to build ...
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1 vote
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What type of ANN architecture to choose?

It sounds like you have structured/tabular data. So, a fully-connected feedforward network should do the job.
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1 vote
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What kind of neural network should I build to classify each instance of a time series sequence?

A time series, usually, requires regular time intervals, but, from looking at your example, it seems that's not the case. You could try to use a MLP and give it as input the Time and Bitrate pairs and ...
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1 vote

Should I use U-net to label keys in a keyboard image?

There are two related problems for images Semantic segmentation, where you need to assign each pixel on the image some class. I.e. you have a satellite image and want to segmentate roads/forests/...
1 vote
Accepted

Should I use U-net to label keys in a keyboard image?

If you just need to draw a rectangle around each key, this is an object detection or template matching problem, so you can use any of the available models for object detection (e.g. YOLO) or any ...
• 37k
1 vote

Which approach should I use to classify points above and below a sine function $y(x) = A + B \sin(Cx)$?

You can try using Fourier basis functions to transform your observable variables and then apply a general linear regression model. To clarify, if you have pairs of observables $(y_i, x_i)$ where $y_i$ ...
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1 vote
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Is there a graph neural network algorithm that can deal with a different number of input and output nodes?

I suggest you look into link prediction. I have had good luck with the StellarGraph library. They have several algorithms implemented, including GCN. Link prediction is a binary classification problem....
1 vote

Is any classifier not subject (or less susceptible) to fooling?

I would say this is not necessarily a duplicate but quite similar to some other questions. However, I will answer the question posed here. At a theoretical level, what you are asking is there any ...
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1 vote

Which model should I use to find (only) the object location (in terms of coordinates) in an image?

What is a good model for this objective? I will try to give another perspective: Solve it without machine learning model Your problem is try to find the most overlapping point. If the image above is ...
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1 vote

Which neural network should I use to approximate a specific but unknown function?

If the concept class specified is $$f(x, y) = k \, \sin(2 \pi f_x x) \, sin(2 \pi f_y y) \\ \land 0 < x < 1 \\ \land 0 < y < 1 \; \text{,}$$ and the optimum fit to example data is ...
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1 vote

Which neural network to use for optical mark recognition?

Is it the case that one of the numbers if filled in? If so a CNN with 10 output should work well. Just choose the output that has the highest probability. If your data allows no number to be filled in,...
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1 vote

Which neural network to use for optical mark recognition?

I'm not familiar with ConvNetSharp library, and the tag convolutional-neural-networks is a bit confusing me, but from : So I've created a neural net using the ...
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1 vote

How can I detect thin objects (like pens and pencils) without a bounding box but only 2 endpoints and the orientation?

Recent work achieves a similar task: Object recognition together with the bounding box (e.g. YOLO---there are quite a few on Github too). The bounding box is not enough in your case, but it is an ...
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1 vote
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Which neural network architectures are there that perform 3D convolutions?

There are many approaches for training CNN on 3d data, but the decision to use a particular architecture is heavily dependant upon the format of your dataset. If you are using 3d point cloud data, I ...

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