Understanding Deep Neural Function Approximation in Reinforcement Learning via ϵ-Greedy Exploration

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Understanding Deep Neural Function Approximation in Reinforcement Learning via ϵ-Greedy Exploration

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Comparison of Reservoir Computing topologies using the Recurrent Kernel approach

Galerkin neural network-POD for acoustic and electromagnetic wave propagation in parametric domains

X-ray Micro-CT based characterization of rock cuttings with deep learning

A coordinate transformation-based physics-informed neural networks for hyperbolic conservation laws

Enhancing wearable sensor data analysis for patient health monitoring using allied data disparity technique and multi instance ensemble perceptron learning

Robust semantic learning for precise medical image segmentation

Nonlinear model reduction with Neural Galerkin schemes on quadratic manifolds

Safe Deep Neural Networks

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Topics in statistical physics of high-dimensional machine learning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Robust machine learning for neuroscientific inference

On the number of regions of piecewise linear neural networks

Random matrix methods for high-dimensional machine learning models