Semiconductor Engineering

How Neural Super Sampling Works: Architecture, Training, And Inference

This blog post is the second in our Neural Super Sampling (NSS) series. The post explores why we introduced NSS and explains its architecture, training, and inference components. In August 2025, we ...
Nature

Neural network compression for reinforcement learning tasks

In real applications of Reinforcement Learning (RL), such as robotics, low latency, energy-efficient and high-throughput inference is very desired. The use of sparsity and pruning for optimizing ...
Nature

Exploring spiking neural networks for deep reinforcement learning in robotic tasks

Spiking Neural Networks (SNNs) are often regarded as the third generation of Artificial Neural Networks (ANNs) because their functionality closely resembles that of the mammalian brain compared to ...
Forbes

Let’s Dance: Structured Movement To Fine-Tune Our Human Neural Nets

Sometimes in the rush to explore our interactions with neural nets (often in the form of LLMs) we forget to think about our own operating system and how it works. Of course, scientists did spend a lot ...
Quanta Magazine

Novel Architecture Makes Neural Networks More Understandable

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...
Forbes

Context Monsters And Dropout Layers: The Job Market As A Neural Net

Forbes contributors publish independent expert analyses and insights. Philip Maymin, a professor of analytics and AI, covers finance and AI. Is this a deep learning neural network, with blue inputs, ...