AI

  Project 2: neural networks that computes the XOR Construct by hand a neural network that computes the XOR function of two inputs. Make sure to specify what sort of units you are using. Show how to build neural network with no hidden layer to implement XOR. If this is not possible, explain why that … Read more

Suppose you had a neural network with linear activation functions. That is, for each unit the output is some constant c times the weighted sum of the inputs.  Assume that the network has one hidden layer. For a given assignment to the weights w, write down equations for the value of the units in the … Read more

Consider a binary classification problem with an ensemble learning algorithm that uses simple majority voting among K learned hypotheses. Suppose that each hypothesis has error E and that the errors made by each hypothesis are independent of the others. Calculate a formula for the error of the ensemble algorithm in terms of K and E, … Read more

Section 19.6.5 noted that the output of the logistic function could be interpreted as a probability p assigned by the model to the proposition that f(x)=1; the probability that f(x)=0 is therefore 1 – p. Write down the probability p as a function of x and calculate the derivative of log p with respect to … Read more

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 Diffusion models are one of the most influential generative modeling techniques for image generation and, across many benchmarks, they outperform state-of-the-art GANs.  Explain your opinion on the reasons for the popularity of diffusion models. What is the real difference between diffusion models and GANs? 

  Generative adversarial nets are mentioned in 2014 by Ian Goodfellow et al.  Why is generative adversarial network a key turning point in the history of generative modeling? Why is the field of image generation important?