Search Results for author: Zhiwei Zeng

Found 14 papers, 5 papers with code

RevMUX: Data Multiplexing with Reversible Adapters for Efficient LLM Batch Inference

1 code implementation6 Oct 2024 Yige Xu, Xu Guo, Zhiwei Zeng, Chunyan Miao

Large language models (LLMs) have brought a great breakthrough to the natural language processing (NLP) community, while leading the challenge of handling concurrent customer queries due to their high throughput demands.

Low-Dimensional Federated Knowledge Graph Embedding via Knowledge Distillation

no code implementations11 Aug 2024 Xiaoxiong Zhang, Zhiwei Zeng, Xin Zhou, Zhiqi Shen

During client-side local training, FedKD facilitates the low-dimensional student model to mimic the score distribution of triples from the high-dimensional teacher model using KL divergence loss.

Knowledge Distillation Knowledge Graph Embedding +1

A Survey on Natural Language Counterfactual Generation

1 code implementation4 Jul 2024 Yongjie Wang, Xiaoqi Qiu, Yu Yue, Xu Guo, Zhiwei Zeng, Yuhong Feng, Zhiqi Shen

Natural language counterfactual generation aims to minimally modify a given text such that the modified text will be classified into a different class.

counterfactual Fairness +1

Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsification

no code implementations19 Jun 2024 Xiaoxiong Zhang, Zhiwei Zeng, Xin Zhou, Dusit Niyato, Zhiqi Shen

Federated Knowledge Graphs Embedding learning (FKGE) encounters challenges in communication efficiency stemming from the considerable size of parameters and extensive communication rounds.

Entity Embeddings Knowledge Graph Embedding +1

Personalized Federated Knowledge Graph Embedding with Client-Wise Relation Graph

no code implementations17 Jun 2024 Xiaoxiong Zhang, Zhiwei Zeng, Xin Zhou, Dusit Niyato, Zhiqi Shen

To address this, we propose Personalized Federated knowledge graph Embedding with client-wise relation Graph (PFedEG), a novel approach that employs a client-wise relation graph to learn personalized embeddings by discerning the semantic relevance of embeddings from other clients.

Entity Embeddings Knowledge Graph Embedding +2

PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning

1 code implementation9 Jun 2024 Xiaoqi Qiu, Yongjie Wang, Xu Guo, Zhiwei Zeng, Yue Yu, Yuhong Feng, Chunyan Miao

Counterfactually Augmented Data (CAD) involves creating new data samples by applying minimal yet sufficient modifications to flip the label of existing data samples to other classes.

Contrastive Learning counterfactual

Are ID Embeddings Necessary? Whitening Pre-trained Text Embeddings for Effective Sequential Recommendation

no code implementations16 Feb 2024 Lingzi Zhang, Xin Zhou, Zhiwei Zeng, Zhiqi Shen

Recent sequential recommendation models have combined pre-trained text embeddings of items with item ID embeddings to achieve superior recommendation performance.

Sequential Recommendation

HGAttack: Transferable Heterogeneous Graph Adversarial Attack

no code implementations18 Jan 2024 He Zhao, Zhiwei Zeng, Yongwei Wang, Deheng Ye, Chunyan Miao

Heterogeneous Graph Neural Networks (HGNNs) are increasingly recognized for their performance in areas like the web and e-commerce, where resilience against adversarial attacks is crucial.

Adversarial Attack

Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition

no code implementations23 Oct 2023 Yige Xu, Zhiwei Zeng, Zhiqi Shen

Emotion Recognition in Conversation (ERC) has been widely studied due to its importance in developing emotion-aware empathetic machines.

Computational Efficiency Emotion Recognition in Conversation

Multimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning

no code implementations21 Mar 2023 Lingzi Zhang, Xin Zhou, Zhiwei Zeng, Zhiqi Shen

We propose a novel Multimodal Pre-training for Sequential Recommendation (MP4SR) framework, which utilizes contrastive losses to capture the correlation among different modality sequences of users, as well as the correlation among different modality sequences of users and items.

Contrastive Learning Representation Learning +1

A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions

2 code implementations9 Feb 2023 HongYu Zhou, Xin Zhou, Zhiwei Zeng, Lingzi Zhang, Zhiqi Shen

Recommendation systems have become popular and effective tools to help users discover their interesting items by modeling the user preference and item property based on implicit interactions (e. g., purchasing and clicking).

Multimodal Recommendation

History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System

no code implementations2 Feb 2023 Tong Zhang, Yong liu, Boyang Li, Zhiwei Zeng, Pengwei Wang, Yuan You, Chunyan Miao, Lizhen Cui

HAHT maintains a long-term memory of history conversations and utilizes history information to understand current conversation context and generate well-informed and context-relevant responses.

Bootstrap Latent Representations for Multi-modal Recommendation

2 code implementations13 Jul 2022 Xin Zhou, HongYu Zhou, Yong liu, Zhiwei Zeng, Chunyan Miao, Pengwei Wang, Yuan You, Feijun Jiang

Besides the user-item interaction graph, existing state-of-the-art methods usually use auxiliary graphs (e. g., user-user or item-item relation graph) to augment the learned representations of users and/or items.

Multi-modal Recommendation

Artificial Persuasion in Pedagogical Games

no code implementations23 Jan 2016 Zhiwei Zeng

With higher level of abstraction, the reusability of the quantitative model is also improved.

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