An efficient neural screening approach rapidly identifies circuit modules governing distinct behavioral transitions in ...
Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...
Intelligent Systems course project (MSc in Computer Engineering @ Unversity of Pisa). Design and development of a MLP, RBF networks and Fuzzy System to estimate person's affective state. Design, ...
Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...
3D rendering—the process of converting three-dimensional models into two-dimensional images—is a foundational technology in computer graphics, widely used across gaming, film, virtual reality, and ...
"For the EstimatorQNN, the expected output shape for the forward pass is (1, num_qubits * num_observables)” In practice, the forward pass returns an array of shape (batch_size, num_observables)—one ...
Artificial intelligence might now be solving advanced math, performing complex reasoning, and even using personal computers, but today’s algorithms could still learn a thing or two from microscopic ...
John Hopfield and Geoffrey Hinton won the Nobel Prize in Physics for their work on artificial neural networks and machine learning. Jonathan Nackstrand / AFP via Getty Images A pair of scientists—John ...
Abstract: Activation functions are pivotal in neural networks, determining the output of each neuron. Traditionally, functions like sigmoid and ReLU have been static and deterministic. However, the ...
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