# Tag Info

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Although I have only partially read (or not read at all) some of the following resources and some of these resources may not cover more advanced topics than the ones presented in the book you are reading, I think they can still be useful for your purposes, so I will share them with you. I would also like to note that if you understand the contents of the ...

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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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There is no contradiction. First, agnostic PAC learnable doesn't mean that the there is a good hypothesis in the hypothesis class; it just means that there is an algorithm that can probably approximately do as well as the best hypothesis in the hypothesis class. Also, these NFL theorems have specific mathematical statements, and hypothesis classes for ...

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VC dimension A rigorous measure of the capacity of a neural network is the VC dimension, which is intuitively a number or bound that quantifies the difficulty of learning from data. The sample complexity, which is the number of training instances that the model (or learner) must be exposed to in order to be reasonably certain of the accurateness of the ...

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Yes, PAC learning can be relevant in practice. There's an area of research that combines PAC learning and Bayesian learning that is called PAC-Bayesian (or PAC-Bayes) learning, where the goal is to find PAC-like bounds for Bayesian estimators. For example, Theorem 1 (McAllester’s bound) of the paper A primer on PAC-Bayesian learning (2019) by Benjamin ...

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As far as I know, the sigmoid is often used as the activation function of the output layer mainly because it is a convenient way of producing an output $p \in [0, 1]$, which can be interpreted as a probability, although that can be misleading or even wrong (if you interpret it as an uncertainty too). You may require the output of the neural network to be a ...

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In computational learning theory, a learning algorithm (or learner) $A$ is an algorithm that chooses a hypothesis (which is a function) $h: \mathcal{X} \rightarrow \mathcal{Y}$, where $\mathcal{X}$ is the input space and $\mathcal{Y}$ is the target space, from the hypothesis space $H$. For example, consider the task of image classification (e.g. MNIST). You ...

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Well, there are some questions here... Does it (Deep Learning) try to learn a continuous distribution based on the training-set and its corresponding mappings, and map unseen examples from this learned distribution? Yes. Talking about Deep Artificial Neural Networks, they try to learn continuous distribution using continuous activation functions in each ...

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Consider a continuum of complexity in models. Trivial: $y = x + a$ Simple: $y = x \, \log \, (a x + b) + c$ Moderately complex: A wind turbine under constant wind velocity Very complex: Ray tracing of lit 3-D motion scenes to pixels Astronomically complex: The weather Now consider a continuum regarding the generality or specificity of models. Very ...

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No, Neural Networks do not have such a guarantee. In fact, I don't believe any kind of classifier in the entire field of Machine Learning has such a guarantee, though some may be slipping my mind... For an easy counterexample, consider what happens if you have two instances with precisely identical inputs, but different output labels. If your classifier is ...

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Multilayer Perceptron (MLP) can theoretically approximate any bounded, continuous function. There's no guarantee for a discontinuous function. There are plenty of important discontinuous functions, like, say, the prime counting function. The prime counting function $\pi(n)$ is simply equal to the number of primes less than or equal to $n$. It has a ...

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Computational learning theory (CLT or COLT) is devoted to the mathematical and computational analysis of machine learning algorithms, so it is concerned with the learnability (i.e. generalization, bounds, efficiency, etc.) of certain tasks, given a learner (or a learning algorithm), a hypothesis space, data, etc. CLT can be divided into (at least) two ...

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Given a hypothesis set $H$, the set of all possible mappings from $X\to Y$ where $X$ is our input space and $Y$ are our binary mappings: $\{-1,1\}$, the growth function, $\Pi_H(m)$, is defined as the maximum number of dichotomies generated by $H$ on $m$ points. Here a dichotomy is the set of $m$ points in $X$ that represent a hypothesis. A hypothesis is just ...

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From [1] we know that we have the following bound between the test and train error for i.i.d samples: $$\mathbb{P}\left(R \leqslant R_{emp} + \sqrt{\frac{d\left(\log{\left(\frac{2m}{d}\right)}+1\right)-\log{\left(\frac{\eta}{4}\right)}}{m}}\right) \geqslant 1-\eta$$ $R$ is the test error, $R_{emp}$ is the training error, $m$ is the size of the training ...

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Some of the books that you mention are often used as reference books in introductory courses to machine learning or artificial intelligence. For example, if I remember correctly, in my introductory course to machine learning, the professor suggested the book Pattern Recognition And Machine Learning (2006) by Bishop, although we never used it during the ...

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Section 5.2 Error Decomposition of the book Understanding Machine Learning: From Theory to Algorithms (2014) gives a description of the approximation error and estimation error in the context of empirical risk minimization (ERM) and, in particular, in the context of the bias-complexity tradeoff (which is strictly related to the bias-variance tradeoff). Error/...

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To answer this, it's helpful to consider the notion of a neural network architecture – in this context, we can think of the architecture as being the network depth (i.e. number of layers), width (i.e. number of nodes in a layer), and some other structural aspects, such as recurrent layers, convolution layers, pool layers, etc. Theory In terms of the ...

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While I'm not familiar with any explicit statements regarding what a Multilayer Perceptron (MLP) cannot learn, I can provide some further detail on the positive statements you made about MLP capabilities: A MLP with a single hidden layer is capable of what is commonly termed 'Universal Function Approximation', i.e. it can approximate any bounded continuous ...

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A hypothesis space/class is the set of functions that the learning algorithm considers when picking one function to minimize some risk/loss functional. The capacity of a hypothesis space is a number or bound that quantifies the size (or richness) of the hypothesis space, i.e. the number (and type) of functions that can be represented by the hypothesis space. ...

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After training, all standard models are deterministic (the process each input goes thru is set). In essence, during training the model attempts to learn the distribution of the training dataset. Whether it is able to depends on the size of the model, if it is big enough, it can simply "memorize" all the training samples and result in perfect accuracy on ...

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For standard NNs, their extrapolation behavior an important aspect for financial applications cannot be controlled due to complex functional forms typically involved. Neural Networks with Asymptotics Control discuss how they overcome this significant limitation and develop a new type of neural networks that incorporate large-value asymptotics, when known, ...

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Definitely, you can find the proof in different resources (for example, in these notes or in the paper that originally proposed PAC learnability, A Theory of the Learnable). However, the intuition behind your question is when the size of the hypothesis increases, if you do not change anything, you can't see more part of the space. Hence, the estimation error ...

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This is a commonly used notation in theoretical computer science. $[m]$ is not the variable $m$, but is instead the set of integers from $1$ to $m$ inclusive. The empirical error equation thus reads in English: The cardinality of a set consisting of the elements $i$ of the set of integers $[m]$ such that the hypothesis given input $x_i$ disagrees with ...

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This may sound counter intuitive but one of the biggest rules of thumb for model capacity in deep learning: IT SHOULD OVERFIT. Once you get a model to overfit, its easier to experiment with regularizations, module replacements, etc. But in general, it gives you a good starting ground.

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Theoretical results Rather than providing a rule of thumb (which can be misleading, so I am not a big fan of them), I will provide some theoretical results (the first one is also reported in paper How many hidden layers and nodes?), from which you may be able to derive your rules of thumb, depending on your problem, etc. Result 1 The paper Learning ...

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We can show that it is not true by a counterexample. For example, $X = \{1,2,3\}$ and $\mathcal H = \{\{\},\{1\},\{2\},\{1,2\}\}$ is the finite set hypothesis class. By definition, in this case, the $\mathcal{VC}$ dimension of $\mathcal H$ over the domain $X$ is $d=2$. Although $A = \{3\} \subset X$, whose size is smaller than the $\mathcal{VC}$ dimension, i....

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Let $\varepsilon$ in (17) is equal to $\sqrt{\frac{4}{n}\left(\log{(2\mathsf{N}(\mathcal{F},n))}-\log{\delta}\right)}$. We have:  P\left(\sup_{f\in\mathcal{F}}|R(f)-R_{emp}(f)| > \sqrt{\frac{4}{n}\left(\log{(2\mathcal{N}(\mathcal{F},n))}-\log{\delta}\right)}\right) \leqslant 2\mathcal{N}(\mathcal{F},n) e^{\frac{-n}{4}\left(\frac{4}{n}\left(\log{(2\...

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The VC dimension represents the capacity (the same Vapnik, the letter V from VC, calls it the "capacity") of a model (or, in general, hypotheses class), so a model with a higher VC dimension has more capacity (i.e. it can represent more functions) than a model with a lower VC dimension. The VC dimension is typically used to provide theoretical bounds e.g. ...

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