# Tag Info

### How does noise affect generalization?

We typically think of machine learning models as modeling two different parts of the training data--the underlying generalizable truth (the signal), and the randomness specific to that dataset (the ...
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### How does noise affect generalization?

Noise in the data, to a reasonable amount, may help the network to generalize better. Sometimes, it has the opposite effect. It partly depends on the kind of noise ("true" vs. artificial). The AI FAQ ...
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### Is pooling a kind of dropout?

Dropout and Max-pooling are performed for different reasons. Dropout is a regularization technique, which affects only the training process (during evaluation, it is not active). The goal of dropout ...
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### How can the generalization error be estimated?

Generalization error is the error obtained by applying a model to data it has not seen before. So, if you want to measure generalization error, you need to remove a subset from your data and don't ...
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### How can we get a differentiable neural network to count things?

Estimating from an observation is a function, but "really counting" is a process. Feed-forward neural networks can learn arbitrary functions from training examples, but they cannot represent ...
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### What are the state-of-the-art results on the generalization ability of deep learning methods?

Introduction The paper Generalization in Deep Learning provides a good overview (in section 2) of several results regarding the concept of generalisation in deep learning. I will try to describe one ...
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### What are the techniques for detecting and preventing overfitting?

Usually you keep track of training loss and validation loss and apply proper regularization technique (such as L1, L2, dropout, DropConnect, etc.). The more interesting technique is to observe your ...
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### Is there any research on models that make predictions by also taking into account the previous predictions?

What you're describing is called a recurrent neural network. There are a large number of designs in this family that all have the ability to remember recent inputs and use them in the processing of ...
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### Why is the validation loss less than the training loss, and what can be said about the effect of the learning rate?

This is very difficult to tell with the information provided, but the phenomenon is something that I have encountered many times before. Sometimes this is not a bad thing, here are some possible ...
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### How does replacing states with latent representations help RL agents?

In short, it is much easier for the agent to learn from a smaller dimensional state space. This is because the agent must also do representation learning; i.e. it must also infer what the state is ...
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### How can my Q-learning agent trained to solve a specific maze generalize to other mazes?

I'm going to assume here that you're using the standard, basic, simple variant of $Q$-learning that can be described as tabular $Q$-learning, where all of your state-action pairs for which you're ...
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### Is pooling a kind of dropout?

I think we would consider regularization and downsampling better in this way: dropout it puts some input value (neuron) for the next layer as 0, which makes the current layer a sparse one. So it ...
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### What does "the expectation is taken across different possible inputs, drawn from the distribution of inputs we expect the system to encounter" mean?

The language used here is confusing me, because it is discussing a "distribution", as in a "probability distribution", but then refers to inputs, which are data gathered from outside of any ...
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### How is the DQN able to generalise the learning to unseen states with such a loss function?

Especially in continuous space, convergence of the value function is mainly a theoretical property. Without seeing enough of the state space, as you suggest, there's no way to ensure that your Q ...
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### Why can neural networks generalize at all?

You've asked a question which is basically one of the most important open questions about neural networks. The answer is a huge mystery - any response to this question which immediately opens with a ...
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### When exactly is a model considered over-parameterized?

Ok so after a little more reading, I am currently satisfy with what I found for this question. Yes, the "under-parameterized" and "over-parameterized" terms do not currently have a widely accepted ...
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### How can the generalization error be estimated?

Error Estimation is a subject with a long history. The test-set method is only one way to estimate generalization error. Others include resubstitution, cross-validation, bootstrap, posterior-...
1 vote

### How does noise affect generalization?

PS: There is already some very good answers provided here, I will merely add to this answers in the hope that someone will find this useful: Introducing noise to a dataset can indeed have a positive ...

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