18 citations · 53 across the 19 of their papers we have counts for
19 papers
MERIT: Memory-Enhanced Retrieval for Interpretable Knowledge Tracing
Runze Li, Kedi Chen, Guwei Feng +3
Knowledge Tracing (KT) models students' evolving knowledge states to predict future performance, serving as a foundation for personalized education. While traditional deep learning…
Markovian Pre-Trained Transformer for Next-Item Recommendation
Cong Xu, Guoliang Li, Jun Wang +1
We introduce the Markovian Pre-trained Transformer (MPT) for next-item recommendation, a transferable model fully pre-trained on synthetic Markov chains, yet capable of achieving s…
Large-Model AI for Near Field Beam Prediction: A CNN-GPT2 Framework for 6G XL-MIMO
Wang Liu, Cunhua Pan, Hong Ren +3
The emergence of extremely large-scale antenna arrays (ELAA) in millimeter-wave (mmWave) communications, particularly in high-mobility scenarios, highlights the importance of near-…
Attention Residual Fusion Network with Contrast for Source-free Domain Adaptation
Renrong Shao, Wei Zhang, Jun Wang
Source-free domain adaptation (SFDA) involves training a model on source domain and then applying it to a related target domain without access to the source data and labels during…
Conditional Pseudo-Supervised Contrast for Data-Free Knowledge Distillation
Renrong Shao, Wei Zhang, Jun wang
Data-free knowledge distillation~(DFKD) is an effective manner to solve model compression and transmission restrictions while retaining privacy protection, which has attracted exte…
Consistent Assistant Domains Transformer for Source-free Domain Adaptation
Renrong Shao, Wei Zhang, Kangyang Luo +2
Source-free domain adaptation (SFDA) aims to address the challenge of adapting to a target domain without accessing the source domain directly. However, due to the inaccessibility…
CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models
Runze Li, Siyu Wu, Jun Wang +1
Knowledge Tracing (KT) aims to model a student's learning state over time and predict their future performance. However, traditional KT methods often face challenges in explainabil…
Collaborative Filtering Meets Spectrum Shift: Connecting User-Item Interaction with Graph-Structured Side Information
Yunhang He, Cong Xu, Jun Wang +1
Graph Neural Networks (GNNs) have demonstrated their superiority in collaborative filtering, where the user-item (U-I) interaction bipartite graph serves as the fundamental data fo…
STAIR: Manipulating Collaborative and Multimodal Information for E-Commerce Recommendation
Cong Xu, Yunhang He, Jun Wang +1
While the mining of modalities is the focus of most multimodal recommendation methods, we believe that how to fully utilize both collaborative and multimodal information is pivotal…
E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning
Zihan Liao, Jun Wang, Hang Yu +3
Processing long contexts is increasingly important for Large Language Models (LLMs) in tasks like multi-turn dialogues, code generation, and document summarization. This paper addr…
D2LLM: Decomposed and Distilled Large Language Models for Semantic Search
Zihan Liao, Hang Yu, Jianguo Li +2
The key challenge in semantic search is to create models that are both accurate and efficient in pinpointing relevant sentences for queries. While BERT-style bi-encoders excel in e…
Data-free Knowledge Distillation for Fine-grained Visual Categorization
Renrong Shao, Wei Zhang, Jianhua Yin +1
Data-free knowledge distillation (DFKD) is a promising approach for addressing issues related to model compression, security privacy, and transmission restrictions. Although the ex…
Explainable Session-based Recommendation via Path Reasoning
Yang Cao, Shuo Shang, Jun Wang +1
This paper explores providing explainability for session-based recommendation (SR) by path reasoning. Current SR models emphasize accuracy but lack explainability, while traditiona…
Multi-View Pre-Trained Model for Code Vulnerability Identification
Xuxiang Jiang, Yinhao Xiao, Jun Wang +1
Vulnerability identification is crucial for cyber security in the software-related industry. Early identification methods require significant manual efforts in crafting features or…
Understanding Adversarial Robustness from Feature Maps of Convolutional Layers
Cong Xu, Wei Zhang, Jun Wang +1
The adversarial robustness of a neural network mainly relies on two factors: model capacity and anti-perturbation ability. In this paper, we study the anti-perturbation ability of…
Data-Free Knowledge Transfer: A Survey
Yuang Liu, Wei Zhang, Jun Wang +1
In the last decade, many deep learning models have been well trained and made a great success in various fields of machine intelligence, especially for computer vision and natural…
Source-Free Domain Adaptation for Semantic Segmentation
Yuang Liu, Wei Zhang, Jun Wang
Unsupervised Domain Adaptation (UDA) can tackle the challenge that convolutional neural network(CNN)-based approaches for semantic segmentation heavily rely on the pixel-level anno…
Zero-shot Adversarial Quantization
Yuang Liu, Wei Zhang, Jun Wang
Model quantization is a promising approach to compress deep neural networks and accelerate inference, making it possible to be deployed on mobile and edge devices. To retain the hi…
Learning from a Lightweight Teacher for Efficient Knowledge Distillation
Yuang Liu, Wei Zhang, Jun Wang
Knowledge Distillation (KD) is an effective framework for compressing deep learning models, realized by a student-teacher paradigm requiring small student networks to mimic the sof…