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Found 7 papers, 4 papers with code
Spectral methods for Neural Integral Equations
Neural integral equations are deep learning models based on the theory of integral equations, where the model consists of an integral operator and the corresponding equation (of the second kind) which is learned through an optimization procedure.
Operator Learning Meets Numerical Analysis: Improving Neural Networks through Iterative Methods
Deep neural networks, despite their success in numerous applications, often function without established theoretical foundations.
Continuous Spatiotemporal Transformers
Modeling spatiotemporal dynamical systems is a fundamental challenge in machine learning.
Deep Neural Networks as the Semi-classical Limit of Topological Quantum Neural Networks: The problem of generalisation
Deep Neural Networks miss a principled model of their operation.
FIMP: Foundation Model-Informed Message Passing for Graph Neural Networks
Foundation models have revolutionized the landscape of Deep Learning (DL), serving as a versatile platform which can be adapted to a wide range of downstream tasks.
Neural Integral Equations
In this paper, we introduce Neural Integral Equations (NIE), a method that learns an unknown integral operator from data through an IE solver.
Neural Integro-Differential Equations
Further, we show that NIDE can decompose dynamics into their Markovian and non-Markovian constituents via the learned integral operator, which we test on fMRI brain activity recordings of people on ketamine.
