Kaiwen Hu

According to our database1, Kaiwen Hu authored at least 14 papers between 2016 and 2025.

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Bibliography

2025
Projection Head is Secretly an Information Bottleneck.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024
Meta Reinforcement Learning for Multi-Task Offloading in Vehicular Edge Computing.
IEEE Trans. Mob. Comput., March, 2024

Understanding the Role of Equivariance in Self-supervised Learning.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Design, Modelling and Experiment of a Spatial Multi-locomotion Tensegrity Mobile Robot.
Proceedings of the Intelligent Robotics and Applications - 17th International Conference, 2024

2023
Disassembling Convolutional Segmentation Network.
Int. J. Comput. Vis., July, 2023

Model Doctor for Diagnosing and Treating Segmentation Error.
Proceedings of the IEEE International Conference on Image Processing, 2023

2022
A Probabilistic Approach for Cooperative Computation Offloading in MEC-Assisted Vehicular Networks.
IEEE Trans. Intell. Transp. Syst., 2022

Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctions.
CoRR, 2022

Cell Segmenter: A General Framework for Multi-modality Cell Segmentation.
Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images, 2022

2021
Gauge Invariant Autoregressive Neural Networks for Quantum Lattice Models.
CoRR, 2021

Asynchronous Deep Reinforcement Learning for Data-Driven Task Offloading in MEC-Empowered Vehicular Networks.
Proceedings of the 40th IEEE Conference on Computer Communications, 2021

2016
Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit.
CoRR, 2016

Angle-based constrained dominance principle in MOEA/D for constrained multi-objective optimization problems.
Proceedings of the IEEE Congress on Evolutionary Computation, 2016

Multi-objective evolutionary algorithms embedded with machine learning - A survey.
Proceedings of the IEEE Congress on Evolutionary Computation, 2016


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