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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55
votes
4answers
14k views

Are neural networks prone to catastrophic forgetting?

Imagine you show a neural network a picture of a lion 100 times and label it with "dangerous", so it learns that lions are dangerous. Now imagine that previously you have shown it millions ...
4
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2answers
567 views

Do convolutional neural networks perform convolution or cross-correlation?

Typically, people say that convolutional neural networks (CNN) perform the convolution operation, hence their name. However, some people have also said that a CNN actually performs the cross-...
19
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3answers
7k views

Where can I find the proof of the universal approximation theorem?

The Wikipedia article for the universal approximation theorem cites a version of the universal approximation theorem for Lebesgue-measurable functions from this conference paper. However, the paper ...
9
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1answer
3k views

What is the time complexity of the forward pass algorithm of a feedforward neural network?

How do I determine the time complexity of the forward pass algorithm of a feedforward neural network? How many multiplications are done to generate the output?
5
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2answers
612 views

How to estimate the capacity of a neural network?

Is it possible to estimate the capacity of a neural network model? If so, what are the techniques involved?
90
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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 ...
33
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4answers
34k views

What is the time complexity for training a neural network using back-propagation?

Suppose that a NN contains $n$ hidden layers, $m$ training examples, $x$ features, and $n_i$ nodes in each layer. What is the time complexity to train this NN using back-propagation? I have a basic ...
11
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3answers
4k views

Why exactly do neural networks require i.i.d. data?

In reinforcement learning, successive states (actions and rewards) can be correlated. An experience replay buffer was used, in the DQN architecture, to avoid training the neural network (NN), which ...
3
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1answer
335 views

Why do we need convolutional neural networks instead of feed-forward neural networks?

Why do we need convolutional neural networks instead of feed-forward neural networks? What is the significance of a CNN? Even a feed-forward neural network will able to solve the image classification ...
34
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5answers
19k views

What is the purpose of an activation function in neural networks?

It is said that activation functions in neural networks help introduce non-linearity. What does this mean? What does non-linearity mean in this context? How does the introduction of this non-...
32
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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?
28
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4answers
4k views

Can neural networks be used to prove conjectures?

Imagine I have a list (in a computer-readable form) of all problems (or statements) and proofs that math relies on. Could I train a neural network in such a way that, for example, I enter a problem ...
5
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1answer
2k views

Is back-propagation applied for each data point or for a batch of data points?

I am new to deep learning and trying to understand the concept of back-propagation. I have a doubt about when the back-propagation is applied. Assume that I have a training data set of 1000 images ...
77
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3answers
55k views

How can neural networks deal with varying input sizes?

As far as I can tell, neural networks have a fixed number of neurons in the input layer. If neural networks are used in a context like NLP, sentences or blocks of text of varying sizes are fed to a ...
34
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3answers
26k 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, ...
22
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3answers
10k views

How to choose an activation function for the hidden layers?

I choose the activation function for the output layer depending on the output that I need and the properties of the activation function that I know. For example, I choose the sigmoid function when I'm ...
30
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7answers
5k views

Is artificial intelligence vulnerable to hacking? [closed]

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. ...
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?
7
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3answers
1k views

How is it possible that the MSE used to train neural networks with gradient descent has multiple local minima?

We often train neural networks by optimizing the mean squared error (MSE), which is an equation of a parabola $y=x^2$, with gradient descent. We also say that weight adjustment in a neural network by ...
36
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5answers
67k views

What is the difference between a convolutional neural network and a regular neural network?

I've seen these terms thrown around this site a lot, specifically in the tags convolutional-neural-networks and neural-networks. I know that a neural network is a system based loosely on the human ...
5
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2answers
4k views

Are neural networks statistical models?

By reading the abstract of Neural Networks and Statistical Models paper it would seem that ANNs are statistical models. In contrast Machine Learning is not just glorified Statistics. I am looking ...
10
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3answers
408 views

Are there any rules of thumb for having some idea of what capacity a neural network needs to have for a given problem?

To give an example. Let's just consider the MNIST dataset of handwritten digits. Here are some things which might have an impact on the optimum model capacity: There are 10 output classes The inputs ...
4
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2answers
260 views

Are Neural Net architectures accidental discoveries?

Recently, I have been learning about new neural networks, which are used for specialized purposes, like speech recognition, image recognition, etc. The more I discover the more I get amazed by the ...
3
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1answer
310 views

Is it possible to build an AGI with neural networks on neuromorphic chips?

I read a lot about the structure of the human brain and artificial neural networks. Is it possible to build an AGI (or human-level AI) with artificial neural networks on neuromorphic chips, which ...
2
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1answer
174 views

Why is tanh a "smoothly" differentiable function?

The sigmoid, tanh, and ReLU are popular and useful activation functions in the literature. The following excerpt taken from p4 of Neural Networks and Neural Language Models says that ...
0
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1answer
135 views

What is a convolutional neural network?

Given that this question has not yet been asked on this site, although similar questions have already been asked in the past (e.g. here or here), what is essentially a convolutional neural network (...
52
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4answers
86k views

How to select number of hidden layers and number of memory cells in an LSTM?

I am trying to find some existing research on how to select the number of hidden layers and the size of these of an LSTM-based RNN. Is there an article where this problem is being investigated, i.e., ...
57
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11answers
10k views

What are some well-known problems where neural networks don't do very well?

Background: It's well-known that neural networks offer great performance across a large number of tasks, and this is largely a consequence of their universal approximation capabilities. However, in ...
32
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5answers
21k 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 ...
26
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4answers
31k views

Can a neural network be used to predict the next pseudo random number?

Is it possible to feed a neural network the output from a random number generator and expect it learn the hashing (or generator) function, so that it can predict what will be the next generated pseudo-...
27
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5answers
12k views

Is it possible to train a neural network as new classes are given?

I would like to train a neural network (NN) where the output classes are not (all) defined from the start. More and more classes will be introduced later based on incoming data. This means that, every ...
34
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4answers
16k views

Could a neural network detect primes?

I am not looking for an efficient way to find primes (which of course is a solved problem). This is more of a "what if" question. So, in theory, could you train a neural network to predict ...
21
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2answers
22k views

What are "bottlenecks" in neural networks?

What are "bottlenecks" in the context of neural networks? This term is mentioned, for example, in this TensorFlow article, which also uses the term "bottleneck values". How does ...
12
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2answers
3k views

What is a recurrent neural network?

Surprisingly, this wasn't asked before - at least I didn't find anything besides some vaguely related questions. So, what is a recurrent neural network, and what are their advantages over regular (or ...
11
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5answers
8k views

What is the fundamental difference between CNN and RNN?

What is the fundamental difference between convolutional neural networks and recurrent neural networks? Where are they applied?
19
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3answers
2k views

How are Artificial Neural Networks and the Biological Neural Networks similar and different?

I've heard multiple times that "Neural Networks are the best approximation we have to model the human brain", and I think it is commonly known that Neural Networks are modelled after our brain. I ...
15
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3answers
36k views

How do I choose the optimal batch size?

Batch size is a term used in machine learning and refers to the number of training examples utilised in one iteration. The batch size can be one of three options: batch mode: where the batch size is ...
14
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3answers
348 views

Why does Batch Normalization work?

Adding BatchNorm layers improves training time and makes the whole deep model more stable. That's an experimental fact that is widely used in machine learning practice. My question is - why does it ...
7
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1answer
647 views

How should the neural network deal with unexpected inputs?

I recently wrote an application using a deep learning model designed to classify inputs. There are plenty of examples of this using images of irises, cats, and other objects. If I trained a data ...
7
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3answers
874 views

What makes the animal brain so special?

Whenever I read any book about neural networks or machine learning, their introductory chapter says that we haven't been able to replicate the brain's power due to its massive parallelism. Now, in ...
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 ...
12
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1answer
2k views

How exactly can ReLUs approximate non-linear and curved functions?

Currently, the most commonly used activation functions are ReLUs. So I answered this question What is the purpose of an activation function in neural networks? and, while writing the answer, it struck ...
5
votes
1answer
413 views

Does training happen during NEAT?

When one uses NEAT to evolve the best fitting network for a task, does training take place in each epoch as well? If I understand correctly, training is the adjustment of the weights of the neural ...
5
votes
3answers
293 views

How is neural architecture search performed?

I have come across something that IBM offers called neural architecture search. You feed it a data set and it outputs an initial neural architecture that you can train. How is neural architecture ...
14
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3answers
2k views

Why are the initial weights of neural networks randomly initialised?

This might sound silly to someone who has plenty of experience with neural networks but it bothers me... Random initial weights might give you better results that would be somewhat closer to what a ...
6
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1answer
237 views

What is the formula used to calculate the loss in the FaceNet model?

The FaceNet model returns the loss of the predictions and ground-truth classes. How is this loss calculated?
6
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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 ...
5
votes
1answer
403 views

Which functions can be activation functions?

What are the required characteristics of an activation function (in a neural network)? Which functions can be activation functions? For example, which of the functions below can be used as an ...
4
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1answer
107 views

Is there anything that ensures that convolutional filters don't end up the same?

I trained a simple model to recognize handwritten numbers from the mnist dataset. Here it is: ...
3
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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 ...