Publications associées à Graph Signal Processing for Machine Learning: A Review and New Perspectives

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Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

Graph Signal Processing for Machine Learning: A Review and New Perspectives

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Robust machine learning for neuroscientific inference

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

From Nano to Macro An overview of the IEEE Bio Image and Signal Processing Technical Committee

Model agnostic methods meta-learn despite misspecifications

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SKA Science Data Challenge 2: analysis and results

Algorithms push forward the application of MALDI-TOF mass fingerprinting in rapid precise diagnosis

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Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

From Nano to Macro An overview of the IEEE Bio Image and Signal Processing Technical Committee

A Framework for Autonomic Computing for In Situ Imageomics

Algorithms push forward the application of MALDI-TOF mass fingerprinting in rapid precise diagnosis

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Quantization for Distributed Processing and Learning of Structured Data

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Robust machine learning for neuroscientific inference

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

Model agnostic methods meta-learn despite misspecifications

SKA Science Data Challenge 2: analysis and results