Publications associées à The Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-based Hand Movement Classification

End-to-end kernel learning via generative random Fourier features

Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the first-stage process, learn-ing an optimal feature map is often formulated as a ...

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Bayes-optimal Learning of Deep Random Networks of Extensive-width

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We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

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Learning ground states of gapped quantum Hamiltonians with Kernel Methods

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The Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-based Hand Movement Classification

Topics in statistical physics of high-dimensional machine learning

End-to-end kernel learning via generative random Fourier features

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Learning ground states of gapped quantum Hamiltonians with Kernel Methods

Kernel Methods and Similarity Learning Applied in Computational Chemistry

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions with Drones

Multikernel Regression with Sparsity Constraint

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Deterministic error bounds for kernel-based learning techniques under bounded noise

Graph Chatbot

Chattez avec Graph Search

End-to-end kernel learning via generative random Fourier features

Topics in statistical physics of high-dimensional machine learning

Kernel Methods and Similarity Learning Applied in Computational Chemistry

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

Multikernel Regression with Sparsity Constraint

Learning ground states of gapped quantum Hamiltonians with Kernel Methods

Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions with Drones

Deterministic error bounds for kernel-based learning techniques under bounded noise