Questions tagged [neural-networks]

For questions about a artificial networks, such as MLPs, CNNs, RNNs, LSTM, and GRU networks, their variants or any other AI system components that qualify as a neural networks in that they are, in part, inspired by biological neural networks.

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90
votes
7answers
16k views

Do scientists know what is happening inside artificial neural networks?

Do scientists or research experts know from the kitchen what is happening inside complex "deep" neural network with at least millions of connections firing at an instant? Do they understand ...
13
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2answers
212 views

Is there any artificial intelligence that possesses "concentration"?

Humans can do multiple tasks at the same (e.g. reading while listening to music), but we memorize information from less focused sources with worse efficiency than we do from our main focus or task. ...
15
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2answers
700 views

How can I automate the choice of the architecture of a neural network for an arbitrary problem?

Assume that I want to solve an issue with a neural network that either I can't fit to existing architectures (perceptron, Konohen, etc) or I'm simply not aware of the existence of those or I'm unable ...
9
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3answers
541 views

Is it beneficial to represent a neural net as a matrix?

A neural network is a directed weighted graph. These can be represented by a (sparse) matrix. Doing so can expose some elegant properties of the network. Is this technique beneficial for examining ...
6
votes
1answer
4k views

How to avoid falling into the "local minima" trap?

How do I avoid my gradient descent algorithm into falling into the "local minima" trap while backpropogating on my neural network? Are there any methods which help me avoid it?
3
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2answers
81 views

Is there a way to define the boundaries of the optimal size of a training set?

At a related question in Computer Science SE, a user told: Neural networks typically require a large training set. Is there a way to define the boundaries of the "optimal" size of a ...
2
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2answers
3k views

Can PHP be considered as a serious programming language for AI? [closed]

I read some information1 about attempts to build neural networks in the PHP programming language. Personally I think PHP is not the right language to do so at all probably because it's a high-level ...
10
votes
1answer
381 views

What are the advantages of complex-valued neural networks?

During my research, I've stumbled upon "complex-valued neural networks", which are neural networks that work with complex-valued inputs (probably weights too). What are the advantages (or simply the ...
40
votes
6answers
21k views

How do capsule neural networks work?

Geoffrey Hinton has been researching something he calls "capsules theory" in neural networks. What is it? How do capsule neural networks work?
11
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2answers
945 views

How much of Deep Mind's work is actually reproducible?

DeepMind has published a lot of works on deep learning in the last years, most of them are state-of-the-art on their respective tasks. But how much of this work has actually been reproduced by the AI ...
10
votes
2answers
621 views

What are the learning limitations of neural networks trained with backpropagation?

In 1969, Seymour Papert and Marvin Minsky showed that Perceptrons could not learn the XOR function. This was solved by the backpropagation network with at least one hidden layer. This type of network ...
12
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3answers
477 views

Are neural networks the only way to reach "true" artificial intelligence?

Currently, most research done in artificial intelligence focuses on neural networks, which have been successfully used to solve many problems. A good example would be DeepMind's AlphaGo, which uses a ...
4
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1answer
79 views

Can abstractive summarization be achieved using neural networks?

Text summarization is a long-standing research problem that was "ignited" by Luhn in 1958. However, a half century later, we still came nowhere close to solving this problem (abstractive ...
6
votes
2answers
2k views

How to write a C decompiler using AI?

I would like to learn more about whether it is possible and how to write a program that decompiles executable binary (an object file) to the C source. I'm not asking exactly 'how', but rather how this ...
16
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2answers
306 views

How do I decide the optimal number of layers for a neural network?

How do I decide the optimal number of layers for a neural network (feedforward or recurrent)?
19
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3answers
2k views

Are there any computational models of mirror neurons?

From Wikipedia: A mirror neuron is a neuron that fires both when an animal acts and when the animal observes the same action performed by another. Mirror neurons are related to imitation learning, ...
32
votes
5answers
22k views

Is it possible to train the neural network to solve math equations?

I'm aware that neural networks are probably not designed to do that, however asking hypothetically, is it possible to train the deep neural network (or similar) to solve math equations? So given the ...
10
votes
2answers
3k views

Can autoencoders be used for supervised learning?

Can autoencoders be used for supervised learning without adding an output layer? Can we simply feed it with a concatenated input-output vector for training, and reconstruct the output part from the ...
2
votes
2answers
295 views

Can we ever achieve hypercomputation using recurrent neural networks?

It is proved that a recurrent neural net with rational weights can be a super-Turing machine. Can we achieve this in practice ?
3
votes
1answer
303 views

In what ways can connectionist AI be integrated with GOFAI?

In what ways can connectionist artificial intelligence (neural networks) be integrated with Good Old-Fashioned A.I. (GOFAI)? For instance, how could deep neural networks be integrated with knowledge ...
3
votes
1answer
131 views

What are the main differences between a deep Boltzmann machine and a deep belief network?

What are the main differences between a deep Boltzmann machine (DBM) (a recurrent neural network) and a deep belief network (which is based on RBMs)?
10
votes
2answers
2k views

What's the difference between hyperbolic tangent and sigmoid neurons?

Two common activation functions used in deep learning are the hyperbolic tangent function and the sigmoid activation function. I understand that the hyperbolic tangent is just a rescaling and ...
4
votes
1answer
242 views

What is the significance of weights in a feedforward neural network?

In a feedforward neural network, the inputs are fed directly to the outputs via a series of weights. What purpose do the weights serve, and how are they significant in this neural network?
31
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2answers
1k views

How is a deep neural network different from other neural networks?

How is a neural network having the "deep" adjective actually distinguished from other similar networks?
1
vote
0answers
110 views

What is the relation between optimality theory and AI?

How do the basic components optimality theory apply to artificial intelligence? How is optimality theory related to neural network research?
25
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4answers
415 views

Is the pattern recognition capability of CNNs limited to image processing?

Can a Convolutional Neural Network be used for pattern recognition in problem domains without image data? For example, by representing abstract data in an image-like format with spatial relations? ...
7
votes
2answers
602 views

Is it possible to implement reinforcement learning using a neural network?

I've implemented the reinforcement learning algorithm for an agent to play snappy bird (a shameless cheap ripoff of flappy bird) utilizing a q-table for storing the history for future lookups. It ...
7
votes
4answers
4k views

What is the purpose of the hidden layers?

Why would anybody want to use "hidden layers"? How do they enhance the learning ability of the network in comparison to the network which doesn't have them (linear models)?
8
votes
1answer
155 views

Can a single neural network handle recognizing two types of objects, or should it be split into two smaller networks?

In particular, an embedded computer (with limited resources) analyzes live video stream from a traffic camera, trying to pick good frames that contain license plate numbers of passing cars. Once a ...
32
votes
4answers
1k views

How to find the optimal number of neurons per layer?

When you're writing your algorithm, how do you know how many neurons you need per single layer? Are there any methods for finding the optimal number of them, or is it a rule of thumb?
14
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3answers
812 views

How does noise affect generalization?

Does increasing the noise in data help to improve the learning ability of a network? Does it make any difference or does it depend on the problem being solved? How is it affect the generalization ...
10
votes
5answers
638 views

What is "backprop"?

What does "backprop" mean? Is the "backprop" term basically the same as "backpropagation" or does it have a different meaning?

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