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

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 ...

Binary neural network with 0/1 invert weights. Trained with evolutionary reinforcement algorithm, at various cycle counts. Swapped memory array is filled with data at inputs and zeros otherwise.

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 ...

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 ...

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.

We’ve already seen the iconic 1993 video game Doom being played on devices ranging from a candy bar to a John Deere tractor to a Lego brick to E. Coli cells. Now, researchers at Google and Tel Aviv ...

In this study we focus on two subnetworks common in the circuitry of swim central pattern generators (CPGs) in the sea slugs, Melibe leonina and Dendronotus iris and show that they are independently ...

The most significant addition to the Java language since Sun Microsystems rewrote the collections API for Java 5 is the introduction of Java records. Java records address two significant pain points ...