Publications associées à Sparse and Parametric Modeling with Applications to Acoustics and Audio

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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

Sparse and Parametric Modeling with Applications to Acoustics and Audio

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Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

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

Deep Learning Approaches for Auditory Perception in Robotics

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Communication-efficient distributed training of machine learning models

MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA

The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry

Learnable Wavelet Packet Transform for Data-Adapted Spectrograms

Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Deep Learning Approaches for Auditory Perception in Robotics

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

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