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1500 questions
6
votes
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Clarifying representation of Neural Nerwork input for Chess Alpha Zero
In the Alpha Zero paper (https://arxiv.org/pdf/1712.01815.pdf) page 13, the input for the NN is described. In the beggining of the page, the authors state that:
"The input to the Neural Network is an N x X x (MT + L) image stack [...]"
From this, I…
Andrew
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6
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2 answers
What exactly are the differences between semantic and lexical-semantic networks?
What exactly are the differences between semantic and lexical-semantic networks?
idontknowwhoiamgodhelpme
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6
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1 answer
Can the quality of randomness in neural network initialization affect model fitting?
This is a topic I have been arguing about for some time now with my colleagues, maybe you could also voice your opinion about it.
Artificial neural networks use random weight initialization within a certain value range. These random parameters are…
Aki Koivu
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Why do feedforward neural networks require the inputs to be of a fixed size, while RNNs can process variable-size inputs?
Why does a vanilla feedforward neural network only accept a fixed input size, while RNNs are capable of taking a series of inputs with no predetermined limit on the size? Can anyone elaborate on this with an example?
Daniel
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6
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How to improve the reward signal when the rewards are sparse?
In cases where the reward is delayed, this can negatively impact a models ability to do proper credit assignment. In the case of a sparse reward, are there ways in which this can be negated?
In a chess example, there are certain moves that you can…
tryingtolearn
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6
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What are the state space and the state transition function in AI?
I'm studying for my AI final exam, and I'm stuck in the state space representation. I understand initial and goal states, but what I don't understand is the state space and state transition function. Can someone explain what are they with…
İsmail Uysal
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6
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1 answer
What are the advantages of RL with actor-critic methods over actor-only methods?
In general, what are the advantages of RL with actor-critic methods over actor-only (or policy-based) methods?
This is not a comparison with the Q-learning series, but probably a method of learning the game with only the actor.
I think it's…
ground clown
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6
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How to express a fully connected neural network succintly using linear algebra?
I'm currently reading the paper Federated Learning with Matched Averaging (2020), where the authors claim:
A basic fully connected (FC) NN can be formulated as: $\hat{y} = \sigma(xW_1)W_2$ [...]
Expanding the preceding expression
$\hat{y} =…
user1360448
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6
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Why do we need importance sampling?
I was studying the off-policy policy improvement method. Then I encountered importance sampling. I completely understood the mathematics behind the calculation, but I am wondering what is the practical example of importance sampling.
For instance,…
Alireza Hosseini
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6
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1 answer
What's the difference between content-based attention and dot-product attention?
I'm following this blog post which enumerates the various types of attention.
It mentions content-based attention where the alignment scoring function for the $j$th encoder hidden state with respect to the $i$th context vector is the cosine…
Alexander Soare
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6
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2 answers
What is the Bellman Equation actually telling?
What does the Bellman equation actually say? And are there many flavours of that?
I get a little confused when I look for the Bellman equation, because I feel like people are telling slightly different things about what it is. And I think the…
Johnny
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6
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What techniques are used to make MDP discrete state space manageable?
Generating a discretized state space for an MDP (Markov Decision Process) model seems to suffer from the curse of dimensionality.
Supposed my state has a few simple features:
Feeling: Happy/Neutral/Sad
Feeling: Hungry/Neither/Full
Food left:…
Brendan Hill
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1 answer
During neural network training, can gradients leak sensitive information in case training data fed is encrypted (homomorphic)?
Some algorithms in the literature allow recovering the input data used to train a neural network. This is done using the gradients (updates) of weights, such as in Deep Leakage from Gradients (2019) by Ligeng Zhu et al.
In case the neural network is…
witdev
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6
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1 answer
What kind of algorithm is the Levenberg–Marquardt algorithm?
Is a Levenberg–Marquardt algorithm a type of back-propagation algorithm or is it a different category of algorithm?
Wikipedia says that it is a curve fitting algorithm. How is a curve fitting algorithm relevant to a neural net?
user3642
6
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0 answers
$\frac{P(x_1 \mid y, s = 1) \dots P(x_n \mid y, s = 1) P(y \mid s = 1)}{P(x \mid s = 1)}$ indicates that naive Bayes learners are global learners?
I am currently studying the paper Learning and Evaluating Classifiers under Sample Selection Bias by Bianca Zadrozny. In section 3. Learning under sample selection bias, the author says the following:
We can separate classifier learners into two…
The Pointer
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