# Tag Info

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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 on ANN gives a good overview. Excerpt: Noise in the actual data is never a good thing, since it limits the accuracy of generalization that can be achieved ...

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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 noise). Fitting both of those parts increases training set accuracy, but fitting the signal also increases test set accuracy (and real-world performance) while ...

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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 train your model on it. After training, you verify your model accuracy (or other performance measures) on the subset you have removed since your model hasn't seen ...

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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 of the results (which is based on concepts from computational or statistical learning theory, so you should expect a technical answer), but I will first ...

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A neural network is composed of continuous functions. Neural networks are regularized by adding an l2 penalty on the weights to the loss function. This means the neural network will try to make the weights as small as possible. The weights are also initiallized with a N(0, 1) distribution so the initial weights will also tend to be small. All of this means ...

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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 probability distribution. Based on the limited information my studying of machine learning has taught me so far, my understanding is that the machine learning algorithm (...

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In the paper Generalization in Unsupervised Learning (2015), Abou-Moustafa and Schuurmans develop an approach to assess the generalization of an unsupervised learning algorithm $A$ on a given dataset $S$ and how to compare the generalization ability of two unsupervised learning algorithms $A_1$ and $A_2$, for the same learning task. They first provide a ...

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The formula $G=\mathbb{E}\left[ f(Z_{T+1}) \mid \mathbf{Z}_1^T\right] - \sum_{t=1}^Tq_t \mathbb{E}\left[ f(Z_t) \mid \mathbf{Z}_1^{t-1} \right]$ actually represents a set, for all possible values of $f$. Therefore, \$\text{disc}(\mathbf{q}) = \operatorname{sup}_{f \in \mathcal{F}} \left( \mathbb{E}\left[ f(Z_{T+1}) \mid \mathbf{Z}_1^T\right] - \sum_{t=1}^Tq_t ...

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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 definitions. Any definition for those term should consider the input data domain as well as the architecture and training procedure. In a recent paper Deep ...

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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 influence on a model. In fact this can be seen as doing the same thing that you would normally do with regularizers like dropout. Some of the example of doing ...

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