Over the last few years the idea of “conditional computation” has been key to making neural network processing more efficient, even though much of the

Hierarchical Reasoning Model (HRM) is a novel approach using two small neural networks recursing at different frequencies. This biologically inspired method beats Large Language models (LLMs) on hard puzzle tasks such as Sudoku, Maze, and A

Explore how Graph Neural Network regression models leverage message passing and advanced loss formulations to predict continuous outcomes accurately

The text discusses confusion about object-oriented programming examples, the lack of practical understanding in neural networks, and criticism of theoretical discussions without practical examples. It also touches on the importance of error control in trading strategies and the need for better explanations and validation methods in machine learning

Read Interdependent Causal Networks for Root Cause Localization from our Data Science & System Security Department

We investigate the influence of the small-world topology on the composition of information flow on networks. By appealing to the combinatorial Hodge theory

(Phys.org)—A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published

CNNs for deep learning

Explore research on learning and memory, focusing on hippocampal and cortical networks, neural oscillations, eye movements, aging, and human–animal translation

This study from Google Brain introduces the Neural GPU, a convolutional gated recurrent unit network, alongside novel training methods that allow neural ne