Search Results for author: Zhihua Wei

Found 15 papers, 4 papers with code

Temporal aware Multi-Interest Graph Neural Network For Session-based Recommendation

no code implementations31 Dec 2021 Qi Shen, Shixuan Zhu, Yitong Pang, Yiming Zhang, Zhihua Wei

Session-based recommendation (SBR) is a challenging task, which aims at recommending next items based on anonymous interaction sequences.

Session-Based Recommendations

Intention Adaptive Graph Neural Network for Category-aware Session-based Recommendation

no code implementations31 Dec 2021 Chuan Cui, Qi Shen, Shixuan Zhu, Yitong Pang, Yiming Zhang, Zhenwei Dong, Zhihua Wei

Session-based recommendation (SBR) is proposed to recommend items within short sessions given that user profiles are invisible in various scenarios nowadays, such as e-commerce and short video recommendation.

Session-Based Recommendations

Multi-Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation

no code implementations22 Dec 2021 Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Bo Long, Jian Pei

As a result, we first propose a more realistic CRS learning setting, namely Multi-Interest Multi-round Conversational Recommendation, where users may have multiple interests in attribute instance combinations and accept multiple items with partially overlapped combinations of attribute instances.

Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph

no code implementations20 Nov 2021 Hanning Gao, Lingfei Wu, Po Hu, Zhihua Wei, Fangli Xu, Bo Long

Finally, we apply an answer selection model on the full KSG and the top-ranked sub-KSGs respectively to validate the effectiveness of our proposed graph-augmented learning to rank method.

Answer Selection Graph Question Answering +2

RDF-to-Text Generation with Reinforcement Learning Based Graph-augmented Structural Neural Encoders

no code implementations20 Nov 2021 Hanning Gao, Lingfei Wu, Po Hu, Zhihua Wei, Fangli Xu, Bo Long

Most previous methods solve this task using a sequence-to-sequence model or using a graph-based model to encode RDF triples and to generate a text sequence.

Text Generation

SpeechT5: Unified-Modal Encoder-Decoder Pre-training for Spoken Language Processing

no code implementations14 Oct 2021 Junyi Ao, Rui Wang, Long Zhou, Shujie Liu, Shuo Ren, Yu Wu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei

Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-training natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning.

Quantization Representation Learning +3

Multi-View Self-Attention Based Transformer for Speaker Recognition

no code implementations11 Oct 2021 Rui Wang, Junyi Ao, Long Zhou, Shujie Liu, Zhihua Wei, Tom Ko, Qing Li, Yu Zhang

In this work, we propose a novel multi-view self-attention mechanism and present an empirical study of different Transformer variants with or without the proposed attention mechanism for speaker recognition.

Speaker Recognition

Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation

no code implementations24 Sep 2021 Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long

In this work, we propose an end-to-end heterogeneous global graph learning framework, namely Graph Learning Augmented Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation.

Graph Learning

Multi-behavior Graph Contextual Aware Network for Session-based Recommendation

no code implementations24 Sep 2021 Qi Shen, Lingfei Wu, Yitong Pang, Yiming Zhang, Zhihua Wei, Fangli Xu, Bo Long

Based on the global graph, MGCNet attaches the global interest representation to final item representation based on local contextual intention to address the limitation (iii).

Session-Based Recommendations

Interpretable Compositional Convolutional Neural Networks

1 code implementation9 Jul 2021 Wen Shen, Zhihua Wei, Shikun Huang, BinBin Zhang, Jiaqi Fan, Ping Zhao, Quanshi Zhang

The reasonable definition of semantic interpretability presents the core challenge in explainable AI.

Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

no code implementations8 Jul 2021 Yitong Pang, Lingfei Wu, Qi Shen, Yiming Zhang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long, Jian Pei

Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions.

Session-Based Recommendations

EfficientTDNN: Efficient Architecture Search for Speaker Recognition

1 code implementation25 Mar 2021 Rui Wang, Zhihua Wei, Haoran Duan, Shouling Ji, Yang Long, Zhen Hong

Compared with hand-designed approaches, neural architecture search (NAS) appears as a practical technique in automating the manual architecture design process and has attracted increasing interest in spoken language processing tasks such as speaker recognition.

Data Augmentation Network Pruning +2

Tongji University Undergraduate Team for the VoxCeleb Speaker Recognition Challenge2020

no code implementations20 Oct 2020 Shufan Shen, Ran Miao, Yi Wang, Zhihua Wei

In this report, we discribe the submission of Tongji University undergraduate team to the CLOSE track of the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2020 at Interspeech 2020.

Data Augmentation Denoising +2

3D-Rotation-Equivariant Quaternion Neural Networks

1 code implementation ECCV 2020 Wen Shen, BinBin Zhang, Shikun Huang, Zhihua Wei, Quanshi Zhang

This paper proposes a set of rules to revise various neural networks for 3D point cloud processing to rotation-equivariant quaternion neural networks (REQNNs).

Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing

1 code implementation CVPR 2021 Wen Shen, Zhihua Wei, Shikun Huang, BinBin Zhang, Panyue Chen, Ping Zhao, Quanshi Zhang

In this paper, we diagnose deep neural networks for 3D point cloud processing to explore utilities of different intermediate-layer network architectures.

Adversarial Robustness

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