Interpretability in deep learning for finance: A case study for the Heston model

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 ...
EurekAlert!

PolyU develops novel AI graph neural network models to unravel interdisciplinary complexities in image recognition and neuroscience

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...

RRAM hasn't delivered yet, but stacked memory is being pitched to run neural networks in place

A team at the University of California, San Diego has redesigned how RRAM operates in an effort to accelerate the execution ...
EurekAlert!

Making light “think” in negatives: A fully real-valued optical chip for generative model

Researchers have developed a fully real-valued, end-to-end optical neural network chip that overcomes the physical limitation ...
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 ...
Ars Technica

Here’s what’s really going on inside an LLM’s neural network

With most computer programs—even complex ones—you can meticulously trace through the code and memory usage to figure out why that program generates any specific behavior or output. That’s generally ...
Frontiers

Multi-modal Learning with Large-scale Models

Robot perception and cognition often rely on the integration of information from multiple sensory modalities, such as vision, ...