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Mathematical Modelling

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Mathematical Modelling

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Research Frontiers in Machine Learning and Neural Network Approximation Theory

Theoretical foundations of deep learning approximations, universal function representation, and optimization landscape analysis.

Implicit Bias in Deep Neural Network Optimization Landscapes
Universal Approximation Beyond Smooth Functions
Geometric Structure Learning in High-Dimensional Neural Manifolds
Approximation-Generalization Trade-offs in Overparameterized Networks
Operator Learning and Functional Approximation Theory
Neural Network Expressivity Through Topological Data Analysis
Sparse Approximation and Feature Selection in Deep Learning
Approximation Complexity of Attention Mechanisms
Mean Field Theory in Large-Scale Neural Ensembles
Dynamical Systems Approximation via Recurrent Neural Architectures

All Mathematical Modelling PhD categories