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 ...

MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced that they have developed a set of quantum algorithms for feedforward neural networks, breaking through the performance ...

A team of researchers in the Netherlands has proposed a new way of designing computer models of the brain—an approach that could also influence future artificial intelligence (AI) systems. In most ...

NAS methods can generally be classified based on tailored designs from the following aspects: search space, search strategy, and evaluation strategy. In particular, search space can be further ...

MicroCloud Hologram Inc. has announced the creation of a noise-resistant Deep Quantum Neural Network (DQNN) architecture, which aims to advance quantum computing and enhance the efficiency of quantum ...

Building neural networks from scratch in Python with NumPy is one of the most effective ways to internalize deep learning fundamentals. By coding forward and backward propagation yourself, you see how ...

A neural-network-based controller adapts in real time to switching reference signals in piezoelectric nano-positioning stages ...

Graph theory and computational modeling reveal that neural network architecture biases the male Caenorhabditis elegans brain toward prioritized sexual behaviors.

Revolutionary technology achieves order-of-magnitude performance gains on standard CPUs, challenging fundamental assumptions about AI infrastructure requirements ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of neural network quantile regression. The goal of a quantile regression problem is to predict a single numeric ...