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1500 questions
5
votes
2 answers
Is pooling a kind of dropout?
If I got well the idea of dropout, it allows improving the sparsity of the information that comes from one layer to another by setting some weights to zero.
On the other hand, pooling, let's say max-pooling, takes the maximum value in a…
nsaura
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5
votes
2 answers
How does the NEAT speciation algorithm work?
I've been reading up on how NEAT (Neuro Evolution of Augmenting Topologies) works and I've got the main idea of it, but one thing that's been bothering me is how you split the different networks into species. I've gone through the algorithm but it…
Aguy
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5
votes
2 answers
Why is the Chinese Room argument such a big deal?
I've been re-reading the Wikipedia article on the Chinese Room argument and I'm... actually quite unimpressed by it. It seems to me to be largely a semantic issue involving the conflation of various meanings of the word "understand". Of course,…
wizzwizz4
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5
votes
1 answer
How do I show that uniform-cost search is a special case of A*?
How do I show that uniform-cost search is a special case of A*? How do I prove this?
dua fatima
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5
votes
4 answers
What is the difference between "mutation" and "crossover"?
In the context of evolutionary computation, in particular genetic algorithms, there are two stochastic operations "mutation" and "crossover". What are the differences between them?
Abbas Ali
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5
votes
1 answer
How is simulated annealing better than hill climbing methods?
In hill climbing methods, at each step, the current solution is replaced with the best neighbour (that is, the neighbour with highest/smallest value). In simulated annealing, "downhills" moves are allowed.
What are the advantages of simulated…
Huma Qaseem
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5
votes
3 answers
What is the actual learning algorithm: back-propagation or gradient descent?
What is the actual learning algorithm: back-propagation or gradient descent (or, in general, the optimization algorithm)?
I am reading through chapter 8 of Parallel Distributed Processing hand book and the title of the chapter is "Learning internal…
Sreedhar Veluri
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5
votes
1 answer
What are the differences between uniform-cost search and greedy best-first search?
What are the differences between the uniform-cost search (UCS) and greedy best-first search (GBFS) algorithms? How would you convert a UCS into a GBFS?
Abbas Ali
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5
votes
7 answers
What are the most instructive movies about artificial intelligence?
The field of AI has expanded profoundly in recent years, as has public awareness and interest. This includes the arts, where fiction about AI has been popular since at least Isaac Asimov. Films on various subjects can be good teaching aids,…
Marosh Fatima
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5
votes
2 answers
How much can the addition of new features improve the performance?
How much can the addition of new features improve the performance of the model during the optimization process?
Let's say I have a total of 10 features. Suppose I start the optimisation process using only 3 features.
Can the addition of the 7…
Miko Diko
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5
votes
2 answers
Why did the L1/L2 regularization technique not improve my accuracy?
I am training a multilayer neural network with 146 samples (97 for the training set, 20 for the validation set, and 29 for the testing set). I am using:
automatic differentiation,
SGD method,
fixed learning rate + momentum term,
logistic…
LVoltz
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5
votes
2 answers
Can artificial intelligence applications be hacked?
Can artificial intelligence (or machine learning) applications or agents be hacked, given that they are software applications, or are all AI applications secure?
ME.
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5
votes
3 answers
Which neural network to use for optical mark recognition?
I've created a neural net using the ConvNetSharp library which has 3 fully connected hidden layers. The first having 35 neurons and the other two having 25 neurons each, each layer with a ReLU layer as the activation function layer.
I'm using this…
Hamza Abdullah
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5
votes
1 answer
How can my Q-learning agent trained to solve a specific maze generalize to other mazes?
I implemented Q-learning to solve a specific maze. However, it doesn't solve other mazes. How could my Q-learning agent be able to generalize to other mazes?
lrosique
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5
votes
1 answer
Should the reward or the Q value be clipped for reinforcement learning
When extending reinforcement learning to the continuous states, continuous action case, we must use function approximators (linear or non-linear) to approximate the Q-value. It is well known that non-linear function approximators, such as neural…
Rui Nian
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