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Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

Graph Chatbot

Chattez avec Graph Search

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

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

Partial discharge localization in power transformer tanks using machine learning methods

Incorporating Projective Geometry into Deep Learning

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

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

Partial discharge localization in power transformer tanks using machine learning methods

Incorporating Projective Geometry into Deep Learning