Search Results for author: Yancheng Wang

Found 11 papers, 7 papers with code

Low-Rank Graph Contrastive Learning for Node Classification

no code implementations14 Feb 2024 Yancheng Wang, Yingzhen Yang

To the best of our knowledge, our theoretical result is among the first to theoretically demonstrate the advantage of low-rank learning in graph contrastive learning supported by strong empirical performance.

Classification Contrastive Learning +2

RecMind: Large Language Model Powered Agent For Recommendation

no code implementations28 Aug 2023 Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, Yingzhen Yang

While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting their generalizability to new recommendation tasks and their ability to leverage external knowledge due to model scale and data size constraints.

Explanation Generation Language Modelling +2

RNAS-CL: Robust Neural Architecture Search by Cross-Layer Knowledge Distillation

no code implementations19 Jan 2023 Utkarsh Nath, Yancheng Wang, Yingzhen Yang

In this paper, we propose Robust Neural Architecture Search by Cross-Layer Knowledge Distillation (RNAS-CL), a novel NAS algorithm that improves the robustness of NAS by learning from a robust teacher through cross-layer knowledge distillation.

Knowledge Distillation Neural Architecture Search

FEC: Fast Euclidean Clustering for Point Cloud Segmentation

1 code implementation16 Aug 2022 Yu Cao, Yancheng Wang, Yifei Xue, Huiqing Zhang, Yizhen Lao

Segmentation from point cloud data is essential in many applications such as remote sensing, mobile robots, or autonomous cars.

Clustering Instance Segmentation +3

Bayesian Robust Graph Contrastive Learning

1 code implementation27 May 2022 Yancheng Wang, Yingzhen Yang

In this work, we propose a novel and robust method, Bayesian Robust Graph Contrastive Learning (BRGCL), which trains a GNN encoder to learn robust node representations.

Contrastive Learning Node Classification

Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning

1 code implementation17 Feb 2022 Kaize Ding, Yancheng Wang, Yingzhen Yang, Huan Liu

In general, the contrastive learning process in GCL is performed on top of the representations learned by a graph neural network (GNN) backbone, which transforms and propagates the node contextual information based on its local neighborhoods.

Contrastive Learning Representation Learning

ECML: An Ensemble Cascade Metric Learning Mechanism towards Face Verification

1 code implementation11 Jul 2020 Fu Xiong, Yang Xiao, Zhiguo Cao, Yancheng Wang, Joey Tianyi Zhou, Jianxi Wu

Embedding RMML into the proposed ECML mechanism, our metric learning paradigm (EC-RMML) can run in the one-pass learning manner.

Face Verification Fine-Grained Visual Recognition +1

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