Shangqi Guo

Orcid: 0000-0003-3181-6881

According to our database1, Shangqi Guo authored at least 18 papers between 2019 and 2023.

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

Timeline

Legend:

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Bibliography

2023
Partial Consistency for Stabilizing Undiscounted Reinforcement Learning.
IEEE Trans. Neural Networks Learn. Syst., December, 2023

PAC-Bayesian offline Meta-reinforcement learning.
Appl. Intell., November, 2023

Adjacency Constraint for Efficient Hierarchical Reinforcement Learning.
IEEE Trans. Pattern Anal. Mach. Intell., April, 2023

Fast Counterfactual Inference for History-Based Reinforcement Learning.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Orientation-Preserving Rewards' Balancing in Reinforcement Learning.
IEEE Trans. Neural Networks Learn. Syst., 2022

State-Temporal Compression in Reinforcement Learning With the Reward-Restricted Geodesic Metric.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

Primitive-contrastive network: data-efficient self-supervised learning from robot demonstration videos.
Appl. Intell., 2022

Biologically Plausible Variational Policy Gradient with Spiking Recurrent Winner-Take-All Networks.
Proceedings of the 33rd British Machine Vision Conference 2022, 2022

2021
Subjective Learning for Open-Ended Data.
CoRR, 2021

CRIL: Continual Robot Imitation Learning via Generative and Prediction Model.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021

2020
Generative Memory for Lifelong Learning.
IEEE Trans. Neural Networks Learn. Syst., 2020

Emergent Inference of Hidden Markov Models in Spiking Neural Networks Through Winner-Take-All.
IEEE Trans. Cybern., 2020

Cycle representation-disentangling network: learning to completely disentangle spatial-temporal features in video.
Appl. Intell., 2020

Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Task Understanding from Confusing Multi-task Data.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Hierarchical Bayesian Inference and Learning in Spiking Neural Networks.
IEEE Trans. Cybern., 2019

Subjectivity Learning Theory towards Artificial General Intelligence.
CoRR, 2019

Transferable Environment Model With Disentangled Dynamics.
IEEE Access, 2019


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