Questions tagged [iid]
For questions about the concept of independent and identically distributed (often shortened as i.i.d., iid or IID) random variables (or samples) and assumption, which is often made in statistics and machine learning.
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Do adversarial samples violate the i.i.d. assumption?
I am trying to understand why adversarial attacks work in theory.
I have read, that the image is perturbed by a special perturbation $X_{adv}=X_1+p$, but i can't find any reference on that ...
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Does deep RL techniques only interested in 'unit transitions' rather than 'whole experience'?
In deep-rl techniques, if I understand correctly, a replay buffer is used in training the neural networks. The purpose of using the replay buffer is to store the experience and send a (sampled) batch ...
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Does it make sense to compare images (samples) with words (features)?
Consider the following paragraphs from the introduction of the chapter named Recurrent Neural Networks from the textbook titled Dive into Deep Learning
So far we encountered two types of data: ...
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What are the different possible usages of the word "i.i.d" in machine learning?
The acronym "iid" stands for "independent and identically distributed". It is a property of a sequence of random variables. You can read here for more details. This question is ...
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Is knowing underlying probability distribution mandatory for deciding iid property of random variables?
Consider the following information regarding iid random variables
The acronym IID stands for "Independent and Identically Distributed".
A sequence of random variables (or random vectors) is ...
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Which of the following probability distribution is generating an iid dataset?
Let $X_1, X_2$ be two discrete random variables. Each random variable takes two values: $1, 2$
The probability distribution $p_1$ over $X_1, X_2$ is given by
$$p_1(X_1=1, X_2 = 1) = \dfrac{1}{4}$$
$$...
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How can we "draw i.i.d" from any probability distribution?
Consider the following paragraph from 2 Learning in High Dimensions in from of the paper titled Geometric Deep Learning Grids, Groups, Graphs, Geodesics, and Gauges
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What are the iid random variables for a dataset in the GAN framework?
I am trying to understand why mean is used for expectation in training Generative Adversarial Networks.
The answer tells that it is due to the law of large numbers which is based on the assumption ...
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How would the performance of federated learning compare to the performance of centralized machine learning when the data is i.i.d.?
How would the performance of federated learning (FL) compare to the performance of centralized machine learning (ML), when the data is independent and identically distributed (i.i.d.)?
Moreover, what ...
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Are training sequences for LMs sampled in an IID fashion?
If I understand correctly, when training language models, we take a document and then chunk the document into a sequences of k tokens. So if the document is of length 30 and k=10, then we'll have 20 ...
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If the i.i.d. assumption holds, shouldn't the training and validation trends be exactly the same?
If the i.i.d. (independent and identically distributed) assumption holds for a training-validation set pair, shouldn't their loss trends be exactly the same, since every batch from the validation set ...
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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 ...
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