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Found 3 papers, 1 papers with code
Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians
We provide a brief introduction to diffusion models for applied mathematicians and statisticians.
Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer
We demonstrate the feasibility of framing a classically learned deep neural network as an energy based model that can be processed on a one-step quantum annealer in order to exploit fast sampling times.
Deep Learning: An Introduction for Applied Mathematicians
This article provides a very brief introduction to the basic ideas that underlie deep learning from an applied mathematics perspective.
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