Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known Heston option pricing model as a benchmark, researchers show that global ...
Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
Researchers from the University of Tokyo in collaboration with Aisin Corporation have demonstrated that universal scaling laws, which describe how the properties of a system change with size and scale ...
Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...
Joint calibration to the Standard & Poor’s 500 (SPX) and Chicago Board Options Exchange (CBOE) Volatility Index (VIX) market data can be computationally burdensome, especially when the standard course ...
From Deep Blue to modern AI, how chess exposed the shift from brute-force machines to learning systems, and why it matters AI ...
Lightweight convolutional neural networks improved lung cancer classification accuracy in histopathological images while ...
Both a wildfire and activity of digital “neurons” exhibit a phase transition from an active to an absorbing phase. Once a system reaches an absorbing phase, it cannot escape from it without outside ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results