Search Results for author: Zhanhui Kang

Found 18 papers, 6 papers with code

DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback

no code implementations24 May 2024 Yiqing Wu, Ruobing Xie, Zhao Zhang, Xu Zhang, Fuzhen Zhuang, Leyu Lin, Zhanhui Kang, Yongjun Xu

Based on the two observations, we propose a novel model that models positive and negative feedback from a frequency filter perspective called Dual-frequency Graph Neural Network for Sign-aware Recommendation (DFGNN).

ID-centric Pre-training for Recommendation

no code implementations6 May 2024 Yiqing Wu, Ruobing Xie, Zhao Zhang, Fuzhen Zhuang, Xu Zhang, Leyu Lin, Zhanhui Kang, Yongjun Xu

Specifically, in pre-training stage, besides the ID-based sequential model for recommendation, we also build a Cross-domain ID-matcher (CDIM) learned by both behavioral and modality information.

Language Modelling Sequential Recommendation

PhD: A Prompted Visual Hallucination Evaluation Dataset

1 code implementation17 Mar 2024 Jiazhen Liu, Yuhan Fu, Ruobing Xie, Runquan Xie, Xingwu Sun, Fengzong Lian, Zhanhui Kang, Xirong Li

The rapid growth of Large Language Models (LLMs) has driven the development of Large Vision-Language Models (LVLMs).

Attribute Common Sense Reasoning +2

EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs

no code implementations5 Mar 2024 Hanlin Tang, Yifu Sun, Decheng Wu, Kai Liu, Jianchen Zhu, Zhanhui Kang

To our best knowledge, we are the first work that achieves almost lossless quantization performance for LLMs under a data-independent setting and our algorithm runs over 10 times faster than the data-dependent methods.

Data Free Quantization

Plug-in Diffusion Model for Sequential Recommendation

1 code implementation5 Jan 2024 Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin, Zhanhui Kang

To address this issue, this paper presents a novel Plug-in Diffusion Model for Recommendation (PDRec) framework, which employs the diffusion model as a flexible plugin to jointly take full advantage of the diffusion-generating user preferences on all items.

Image Generation Model Optimization +1

Truth Forest: Toward Multi-Scale Truthfulness in Large Language Models through Intervention without Tuning

1 code implementation29 Dec 2023 Zhongzhi Chen, Xingwu Sun, Xianfeng Jiao, Fengzong Lian, Zhanhui Kang, Di Wang, Cheng-Zhong Xu

We introduce Truth Forest, a method that enhances truthfulness in LLMs by uncovering hidden truth representations using multi-dimensional orthogonal probes.

E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity

no code implementations24 Oct 2023 Yun Li, Lin Niu, Xipeng Zhang, Kai Liu, Jianchen Zhu, Zhanhui Kang

Traditional pruning methods are known to be challenging to work in Large Language Models (LLMs) for Generative AI because of their unaffordable training process and large computational demands.

Language Modelling Large Language Model

Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language

no code implementations20 Oct 2023 Zekai Qu, Ruobing Xie, Chaojun Xiao, Yuan YAO, Zhiyuan Liu, Fengzong Lian, Zhanhui Kang, Jie zhou

With the thriving of pre-trained language model (PLM) widely verified in various of NLP tasks, pioneer efforts attempt to explore the possible cooperation of the general textual information in PLM with the personalized behavioral information in user historical behavior sequences to enhance sequential recommendation (SR).

Informativeness Language Modelling +1

TeachCLIP: Multi-Grained Teaching for Efficient Text-to-Video Retrieval

no code implementations2 Aug 2023 Kaibin Tian, Ruixiang Zhao, Hu Hu, Runquan Xie, Fengzong Lian, Zhanhui Kang, Xirong Li

For efficient T2VR, we propose TeachCLIP with multi-grained teaching to let a CLIP4Clip based student network learn from more advanced yet computationally heavy models such as X-CLIP, TS2-Net and X-Pool .

Retrieval text similarity +2

Multi-Feature Integration for Perception-Dependent Examination-Bias Estimation

1 code implementation27 Feb 2023 Xiaoshu Chen, Xiangsheng Li, Kunliang Wei, Bin Hu, Lei Jiang, Zeqian Huang, Zhanhui Kang

Eliminating examination bias accurately is pivotal to apply click-through data to train an unbiased ranking model.

BagFormer: Better Cross-Modal Retrieval via bag-wise interaction

no code implementations29 Dec 2022 Haowen Hou, Xiaopeng Yan, Yigeng Zhang, Fengzong Lian, Zhanhui Kang

In the field of cross-modal retrieval, single encoder models tend to perform better than dual encoder models, but they suffer from high latency and low throughput.

Cross-Modal Retrieval Retrieval

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities

3 code implementations13 Dec 2022 Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Guo, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan

The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework.


MKQ-BERT: Quantized BERT with 4-bits Weights and Activations

no code implementations25 Mar 2022 Hanlin Tang, Xipeng Zhang, Kai Liu, Jianchen Zhu, Zhanhui Kang

In this work, we propose MKQ-BERT, which further improves the compression level and uses 4-bits for quantization.


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