Publications related to On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

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In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

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On the Generalization of Stochastic Gradient Descent with Momentum

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On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent

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Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

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

Random matrix methods for high-dimensional machine learning models

Understanding generalization and robustness in modern deep learning

Enabling Uncertainty Estimation in Iterative Neural Networks

Residual-based attention in physics-informed neural networks

On the Generalization of Stochastic Gradient Descent with Momentum

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Topics in statistical physics of high-dimensional machine learning

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

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

Enabling Uncertainty Estimation in Iterative Neural Networks

Residual-based attention in physics-informed neural networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Understanding generalization and robustness in modern deep learning

Random matrix methods for high-dimensional machine learning models

On the Generalization of Stochastic Gradient Descent with Momentum

Topics in statistical physics of high-dimensional machine learning