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1500 questions
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What are hyper-heuristics, and how are they different from meta-heuristics?
I wanted to know what the differences between hyper-heuristics and meta-heuristics are, and what their main applications are. Which problems are suited to be solved by hyper-heuristics?
bmwalide
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Counterexamples to the reward hypothesis
On Sutton and Barto's RL book, the reward hypothesis is stated as
that all of what we mean by goals and purposes can be well thought of as the maximization of the expected value of the cumulative sum of a received scalar signal (called…
Bananin
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Are humans superior to machines in chess?
A friend of mine, who is an International Master at chess, told me that humans were superior to machines provided you didn't impose the time constraints that exist in competitive chess (40 moves in 2 hours) since very often games were lost, to…
grandtout
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What are all the different kinds of neural networks used for?
I found the following neural network cheat sheet (Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data).
What are all these different kinds of neural networks used for? For example, which neural networks can be used for…
Dan D.
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Why is the derivative of the activation functions in neural networks important?
I'm new to NN. I am trying to understand some of its foundations. One question that I have is: why the derivative of an activation function is important (not the function itself), and why it's the derivative which is tied to how the network performs…
Mary
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5 answers
Why do we need common sense in AI?
Let's consider this example:
It's John's birthday, let's buy him a kite.
We humans most likely would say the kite is a birthday gift, if asked why it's being bought; and we refer to this reasoning as common sense.
Why do we need this in…
Titan
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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 applications) of this kind of neural network over…
rcpinto
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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 community? For instance, the Neural Turing…
rcpinto
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3 answers
What is the relation between semi-supervised and self-supervised visual representation learning?
What's the differences between semi-supervised learning and self-supervised visual representation learning, and how they are connected?
0x90
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Is REINFORCE the same as 'vanilla policy gradient'?
I don't know what people mean by 'vanilla policy gradient', but what comes to mind is REINFORCE, which is the simplest policy gradient algorithm I can think of. Is this an accurate statement?
By REINFORCE I mean this surrogate objective
$$…
yewang
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4 answers
What are the purposes of autoencoders?
Autoencoders are neural networks that learn a compressed representation of the input in order to later reconstruct it, so they can be used for dimensionality reduction. They are composed of an encoder and a decoder (which can be separate neural…
nbro
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5 answers
What is "backprop"?
What does "backprop" mean? Is the "backprop" term basically the same as "backpropagation" or does it have a different meaning?
kenorb
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11
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3 answers
Is it difficult to learn the rotated bounding box for a (rotated) object?
I have checked out many methods and papers, like YOLO, SSD, etc., with good results in detecting a rectangular box around an object, However, I could not find any paper that shows a method that learns a rotated bounding box.
Is it difficult to learn…
Ankish Bansal
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11
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3 answers
What is a deep neural network?
What is the definition of a deep neural network? Why are they so popular or important?
baranskistad
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Why is the n-step tree backup algorithm an off-policy algorithm?
In reinforcement learning book from Sutton & Barto (2018 edition), specifically in section 7.5 of the book, they present an n-step off-policy algorithm that doesn't require importance sampling called n-step tree backup algorithm.
In other…
Brale
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