Search Results for author: Yejia Liu

Found 7 papers, 4 papers with code

ProSGNeRF: Progressive Dynamic Neural Scene Graph with Frequency Modulated Auto-Encoder in Urban Scenes

no code implementations14 Dec 2023 Tianchen Deng, Siyang Liu, Xuan Wang, Yejia Liu, Danwei Wang, Weidong Chen

Implicit neural representation has demonstrated promising results in view synthesis for large and complex scenes.

MetaLDC: Meta Learning of Low-Dimensional Computing Classifiers for Fast On-Device Adaption

1 code implementation23 Feb 2023 Yejia Liu, Shijin Duan, Xiaolin Xu, Shaolei Ren

Fast model updates for unseen tasks on intelligent edge devices are crucial but also challenging due to the limited computational power.

Meta-Learning

Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning

1 code implementation16 Oct 2022 Yejia Liu, Wang Zhu, Shaolei Ren

To provide an approximate solution to this problem in the online continual learning setting, we further propose the Global Pseudo-task Simulation (GPS), which mimics future catastrophic forgetting of the current task by permutation.

Combinatorial Optimization Continual Learning

LeHDC: Learning-Based Hyperdimensional Computing Classifier

1 code implementation18 Mar 2022 Shijin Duan, Yejia Liu, Shaolei Ren, Xiaolin Xu

Thanks to the tiny storage and efficient execution, hyperdimensional Computing (HDC) is emerging as a lightweight learning framework on resource-constrained hardware.

Enabling SQL-based Training Data Debugging for Federated Learning

no code implementations26 Aug 2021 Yejia Liu, Weiyuan Wu, Lampros Flokas, Jiannan Wang, Eugene Wu

The SQL-based training data debugging framework has proved effective to fix this kind of issue in a non-federated learning setting.

Federated Learning

Model Trees for Identifying Exceptional Players in the NHL Draft

1 code implementation23 Feb 2018 Oliver Schulte, Yejia Liu, Chao Li

Successful previous approaches have built a predictive model based on player features, or derived performance predictions from the observed performance of comparable players in a cohort.

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