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1

My understanding is that AI can be understood as a very generalized and abstract statistics software package handling input data in a general way to find the "best fit" to some form of problem. Is that correct? I know it isn't. But is it vaguely correct? No. It's not correct, in my opinion, not even vaguely and in many ways. AI is not (...


3

In general, what are the advantages of RL with actor-critic methods over actor-only (or policy-based) methods? One practical benefit is that critics can use TD learning to bootstrap, allowing them to learn online on each step taken, plus learn in continuing problems. Pure actor algorithms like REINFORCE, cross-entropy method, and non-RL policy-only learners,...


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The probability density is used to 'measure how good' the parameters are because it is a natural way of quantifying if these parameters are good for the observed data. Also, as the notation often causes some confusion, $L(\theta | x)$ denotes the probability of all of your observed data, not just one value. Also the "$|$" may cause confusion as it ...


1

GANs generally produce better photo-realistic images but can be difficult to work with. Conversely, VAEs are easier to train but don’t usually give the best results. I recommend picking VAEs if you don’t have a lot of time to experiment with GANs and photorealism isn’t paramount. There are exceptions such as Google’s VQ-VAE 2 which can compete with GANs for ...


1

The difference is much simpler than you might have anticipated: In the quantum computing community, machine learning algorithms designed to be used on quantum computers as opposed to classical computers, would fall under "quantum machine learning". There's really nothing more to it! There is a short paper published in Nature called "Quantum ...


0

Every model is a function. Not every function is a model. A function uniquely maps elements of some set to elements of another set, possibly the same set. Every AI model is a function because they are implemented as computer programs and every computer program is a function uniquely mapping the combination of the sequence of bits in memory and storage at ...


3

The first two equations are equivalent. The last equation can be equivalent if you scale $\alpha$ appropriately. Equation 1 Consider the equation from the Stanford slide: $$ v_{t}=\rho v_{t-1}+\nabla f(x_{t-1}) \\ x_{t}=x_{t-1}-\alpha v_{t}, $$ Let's evaluate the first few $v_t$ so that we can arrive at a closed form solution: $v_0 = 0 \\ v_1 = \rho v_0 + ...


1

Any model can be considered to be a function. The term "model" simply denotes a function being used in a particular way, namely to approximate some other function of interest.


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In simple terms, a neural network model is a function approximator which tries to fit the curve of the hypothesis function. A function itself has an equation which will generate a fixed curve: If we have the equation (i.e., the function), we do not need neural network for its input data. However, when we only have some notion of its curve (or the input and ...


5

Although this may not be applicable to all cases, I like to think of a model as a set of functions, so here's the difference. Why is this definition useful? If you think of a neural network with a vector of parameters $\theta \in \mathbb{R}^m$ as a model, then a specific combination of these parameters represents a specific function. For example, suppose ...


0

Spectral Graph Convolution We use the Convolution Theorem to define convolution for graphs. The Convolution Theorem states that the Fourier transform of the convolution of two functions is the pointwise product of their Fourier transforms: $$\mathcal{F}(w*h) = \mathcal{F}(w) \odot \mathcal{F}(h) \tag{1}\label{1} $$ $$ w * h = \mathcal{F}^{-1}(\mathcal{F}(w)\...


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