Search Results for author: Chi-Man Vong

Found 5 papers, 0 papers with code

Sparse Bayesian Learning with Diagonal Quasi-Newton Method for Large Scale Classification

no code implementations17 Jul 2021 Jiahua Luo, Chi-Man Vong, Jie Du

Sparse Bayesian Learning (SBL) constructs an extremely sparse probabilistic model with very competitive generalization.

Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph

no code implementations30 Apr 2021 Chi-Man Wong, Fan Feng, Wen Zhang, Chi-Man Vong, Hui Chen, Yichi Zhang, Peng He, Huan Chen, Kun Zhao, Huajun Chen

We first construct a billion-scale conversation knowledge graph (CKG) from information about users, items and conversations, and then pretrain CKG by introducing knowledge graph embedding method and graph convolution network to encode semantic and structural information respectively. To make the CTR prediction model sensible of current state of users and the relationship between dialogues and items, we introduce user-state and dialogue-interaction representations based on pre-trained CKG and propose K-DCN. In K-DCN, we fuse the user-state representation, dialogue-interaction representation and other normal feature representations via deep cross network, which will give the rank of candidate items to be recommended. We experimentally prove that our proposal significantly outperforms baselines and show it's real application in Alime.

Click-Through Rate Prediction Knowledge Graph Embedding +1

Persistent Homology Based Graph Convolution Network for Fine-Grained 3D Shape Segmentation

no code implementations ICCV 2021 Chi-Chong Wong, Chi-Man Vong

Current deep learning and graph machine learning methods fail to tackle such challenges and thus provide inferior performance in fine-grained 3D analysis.

Topological Data Analysis

3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention

no code implementations17 May 2019 Zhizhong Han, Xiyang Wang, Chi-Man Vong, Yu-Shen Liu, Matthias Zwicker, C. L. Philip Chen

Then, the content and spatial information of each pair of view nodes are encoded by a novel spatial pattern correlation, where the correlation is computed among latent semantic patterns.

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