Yuanhao Li

Orcid: 0000-0002-6938-5012

Affiliations:
  • Tokyo Institute of Technology, Yokohama, Japan


According to our database1, Yuanhao Li authored at least 13 papers between 2018 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2025
Sparse Bayesian correntropy learning for robust muscle activity reconstruction from noisy brain recordings.
Neural Networks, 2025

2024
Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging.
CoRR, 2024

2023
Correntropy-Based Logistic Regression With Automatic Relevance Determination for Robust Sparse Brain Activity Decoding.
IEEE Trans. Biomed. Eng., August, 2023

Skeleton-aware implicit function for single-view human reconstruction.
CAAI Trans. Intell. Technol., June, 2023

Adaptive sparseness for correntropy-based robust regression via automatic relevance determination.
Proceedings of the International Joint Conference on Neural Networks, 2023

2022
A CW-CNN regression model-based real-time system for virtual hand control.
Frontiers Neurorobotics, September, 2022

Restricted Minimum Error Entropy Criterion for Robust Classification.
IEEE Trans. Neural Networks Learn. Syst., 2022

2021
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals.
CoRR, 2021

2020
Common Spatial Patterns Based on the Quantized Minimum Error Entropy Criterion.
IEEE Trans. Syst. Man Cybern. Syst., 2020

2019
Robust Logistic Regression against Attribute and Label Outliers via Information Theoretic Learning.
CoRR, 2019

2018
Bias-compensated normalized maximum correntropy criterion algorithm for system identification with noisy input.
Signal Process., 2018

Sparse Normalized Least Mean Absolute Deviation Algorithm Based on Unbiasedness Criterion for System Identification With Noisy Input.
IEEE Access, 2018

Robust Locality Preserving Projection Based on Kernel Risk-Sensitive Loss.
Proceedings of the 2018 International Joint Conference on Neural Networks, 2018


  Loading...