Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

I followed a Bob Ross Painting tutorial but instead of using paintbrushes, I used a bunch of random objects like a Snickers bar, a bag of Cheetos, and a toy car. Needless to say it didn't go fantastic ...

It has been proposed by E. Gelenbe in 1989. A Random Neural Network is a compose of Random Neurons and Spikes that circulates through the network. According to this model, each neuron has a positive ...

Abstract: Simulation is an excellent tool to study real-life systems with uncertainty. Discrete-event simulation (DES) is a common simulation approach to model time-dependent and complex systems.