Yan Zeng

Orcid: 0000-0001-7721-2560

Affiliations:
  • Guangdong University of Technology, Guangzhou, China


According to our database1, Yan Zeng authored at least 14 papers between 2019 and 2024.

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

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2024
Exemplar-Free Class Incremental Learning via Incremental Representation.
CoRR, 2024

ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization.
CoRR, 2024

Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy.
CoRR, 2024

eTag: Class-Incremental Learning via Embedding Distillation and Task-Oriented Generation.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Nonlinear Causal Discovery for High-Dimensional Deterministic Data.
IEEE Trans. Neural Networks Learn. Syst., May, 2023

Causal discovery of 1-factor measurement models in linear latent variable models with arbitrary noise distributions.
Neurocomputing, March, 2023

eTag: Class-Incremental Learning with Embedding Distillation and Task-Oriented Generation.
CoRR, 2023

A Survey on Causal Reinforcement Learning.
CoRR, 2023

2022
Causal Discovery for Linear Mixed Data.
Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2021
Causal Discovery with Multi-Domain LiNGAM for Latent Factors.
Proceedings of the Causal Analysis Workshop Series, 2021

2020
An Efficient Entropy-Based Causal Discovery Method for Linear Structural Equation Models With IID Noise Variables.
IEEE Trans. Neural Networks Learn. Syst., 2020

A causal discovery algorithm based on the prior selection of leaf nodes.
Neural Networks, 2020

Spike Sorting Based On Low-Rank And Sparse Representation.
Proceedings of the IEEE International Conference on Multimedia and Expo, 2020

2019
Causal Discovery of Linear Non-Gaussian Acyclic Model with Small Samples.
Proceedings of the Intelligence Science and Big Data Engineering. Big Data and Machine Learning, 2019


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