1 citations · 2 across the 14 of their papers we have counts for
15 papers
Farewell to Item IDs: Unlocking the Scaling Potential of Large Ranking Models via Semantic Tokens
Zhen Zhao, Tong Zhang, Jie Xu +5
Recent studies on scaling up ranking models have achieved substantial improvement for recommendation systems and search engines. However, most large-scale ranking systems rely on i…
Streaming-dLLM: Accelerating Diffusion LLMs via Suffix Pruning and Dynamic Decoding
Zhongyu Xiao, Zhiwei Hao, Jianyuan Guo +4
Diffusion Large Language Models (dLLMs) offer a compelling paradigm for natural language generation, leveraging parallel decoding and bidirectional attention to achieve superior gl…
Noise-Adaptive Layerwise Learning Rates: Accelerating Geometry-Aware Optimization for Deep Neural Network Training
Jie Hao, Xiaochuan Gong, Jie Xu +2
Geometry-aware optimization algorithms, such as Muon, have achieved remarkable success in training deep neural networks (DNNs). These methods leverage the underlying geometry of DN…
Can LLMs Refuse Questions They Do Not Know? Measuring Knowledge-Aware Refusal in Factual Tasks
Wenbo Pan, Jie Xu, Qiguang Chen +5
Large Language Models (LLMs) should refuse to answer questions beyond their knowledge. This capability, which we term knowledge-aware refusal, is crucial for factual reliability, w…
Dynamic Policy Induction for Adaptive Prompt Optimization: Bridging the Efficiency-Accuracy Gap via Lightweight Reinforcement Learning
Jiexi Xu
The performance of Large Language Models (LLMs) depends heavily on the chosen prompting strategy, yet static approaches such as Zero-Shot, Few-Shot, or Chain-of-Thought (CoT) impos…
Memory Management and Contextual Consistency for Long-Running Low-Code Agents
Jiexi Xu
The rise of AI-native Low-Code/No-Code (LCNC) platforms enables autonomous agents capable of executing complex, long-duration business processes. However, a fundamental challenge r…
Agentic Metacognition: Designing a "Self-Aware" Low-Code Agent for Failure Prediction and Human Handoff
Jiexi Xu
The inherent non-deterministic nature of autonomous agents, particularly within low-code/no-code (LCNC) environments, presents significant reliability challenges. Agents can become…
Generative AI Meets Wireless Sensing: Towards Wireless Foundation Model
Zheng Yang, Guoxuan Chi, Chenshu Wu +5
Generative Artificial Intelligence (GenAI) has made significant advancements in fields such as computer vision (CV) and natural language processing (NLP), demonstrating its capabil…
Contamination Means Overestimation? A Fine-Grained Empirical Study in Code Intelligence
Zhen Yang, Hongyi Lin, Yifan He +7
In recent years, code intelligence has gained increasing importance in the field of automated software engineering. Meanwhile, the widespread adoption of Pretrained Language Models…
Rethinking the generalization of drug target affinity prediction algorithms via similarity aware evaluation
Chenbin Zhang, Zhiqiang Hu, Chuchu Jiang +3
Drug-target binding affinity prediction is a fundamental task for drug discovery. It has been extensively explored in literature and promising results are reported. However, in thi…
Incomplete Multi-view Clustering via Diffusion Contrastive Generation
Yuanyang Zhang, Yijie Lin, Weiqing Yan +6
Incomplete multi-view clustering (IMVC) has garnered increasing attention in recent years due to the common issue of missing data in multi-view datasets. The primary approach to ad…
Dynamic Noise Preference Optimization: Self-Improvement of Large Language Models with Self-Synthetic Data
Haoyan Yang, Khiem Le, Ting Hua +7
Although LLMs have achieved significant success, their reliance on large volumes of human-annotated data has limited their potential for further scaling. In this situation, utilizi…
All-in-One Tuning and Structural Pruning for Domain-Specific LLMs
Lei Lu, Zhepeng Wang, Runxue Bao +7
Existing pruning techniques for large language models (LLMs) targeting domain-specific applications typically follow a two-stage process: pruning the pretrained general-purpose LLM…
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation
Jie Xu, Karthikeyan Saravanan, Rogier van Dalen +3
Federated learning (FL) allows clients to collaboratively train a global model without sharing their local data with a server. However, clients' contributions to the server can sti…
MV2MAE: Multi-View Video Masked Autoencoders
Ketul Shah, Robert Crandall, Jie Xu +4
Videos captured from multiple viewpoints can help in perceiving the 3D structure of the world and benefit computer vision tasks such as action recognition, tracking, etc. In this p…