Xingyu Zhao

Orcid: 0000-0001-6541-3445

Affiliations:
  • Southeast University, Nanjing, China
  • China University of Mining and Technology, Xuzhou, China (former)


According to our database1, Xingyu Zhao authored at least 17 papers between 2018 and 2023.

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Bibliography

2023
From Instance to Metric Calibration: A Unified Framework for Open-World Few-Shot Learning.
IEEE Trans. Pattern Anal. Mach. Intell., August, 2023

Continuous label distribution learning.
Pattern Recognit., 2023

Scalable Label Distribution Learning for Multi-Label Classification.
CoRR, 2023

Generalizable Label Distribution Learning.
Proceedings of the 31st ACM International Conference on Multimedia, 2023

Learning to Learn from Corrupted Data for Few-Shot Learning.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Imbalanced Label Distribution Learning.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Fusion Label Enhancement for Multi-Label Learning.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021
Conditional Self-Supervised Learning for Few-Shot Classification.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

2020
Robust spike-and-slab deep Boltzmann machines for face denoising.
Neural Comput. Appl., 2020

2019
BEMD image fusion based on PCNN and compressed sensing.
Soft Comput., 2019

Applications of asynchronous deep reinforcement learning based on dynamic updating weights.
Appl. Intell., 2019

An effective asynchronous framework for small scale reinforcement learning problems.
Appl. Intell., 2019

A new asynchronous reinforcement learning algorithm based on improved parallel PSO.
Appl. Intell., 2019

A review on multi-class TWSVM.
Artif. Intell. Rev., 2019

2018
深度强化学习研究综述 (Research on Deep Reinforcement Learning).
计算机科学, 2018

NSCT-PCNN image fusion based on image gradient motivation.
IET Comput. Vis., 2018

Asynchronous reinforcement learning algorithms for solving discrete space path planning problems.
Appl. Intell., 2018


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