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Neural Networks and Deep Learning

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An Information-Theoretic View for Deep LearningPreprint

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Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification ProblemsPreprint

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Deep learning fluid flow reconstruction around arbitrary two-dimensional objects from sparse sensors using conformal mappings

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Mode Recognition of Rectangular Dielectric Resonator Antenna Using Artificial Neural Network

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Universal Approximation Abilities of a Modular Differentiable Neural Network

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Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer

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Quantum-Interference Artificial Neural Network With Application to Space Manipulator Control

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Transfer learning approach in deep neural networks for uterine fibroid detection

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A Proof of Concept Study of Using Machine-Learning in Artificial Aortic Valve Design: From Leaflet Design to Stress Analysis

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Understanding How Pretraining Regularizes Deep Learning Algorithms

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Autoencoder-driven spiral representation learning for gravitational wave surrogate modelling

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Approximating Reachable Sets for Neural Network-Based Models in Real Time via Optimal Control

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Computer vision for automated seizure detection and classification: A systematic review

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Neural Network with Unbounded Activations is Universal Approximator

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Constructive Universal Approximation by Infinite-Dimensional Neural Networks via Gaussian-Weighted Ridgelet TransformPreprint

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Approximation capabilities of neural networks on unbounded domains

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ReLU Network with Bounded Width Is a Universal Approximator in View of an Approximate Identity

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