Publications (27)
Legommenders: A Comprehensive Content-Based Recommendation Library with LLM Support
Qijiong Liu, Lu Fan, Xiao-Ming Wu
We present Legommenders, a unique library designed for content-based recommendation that enables the joint training of content encoders alongside behavior and interaction modules,…
Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization
Qijiong Liu, Lu Fan, Jiaren Xiao +2
Category information plays a crucial role in enhancing the quality and personalization of recommender systems. Nevertheless, the availability of item category information is not co…
Evaluating Recabilities of Foundation Models: A Multi-Domain, Multi-Dataset Benchmark
Qijiong Liu, Jieming Zhu, Yingxin Lai +5
Comprehensive evaluation of the recommendation capabilities of existing foundation models across diverse datasets and domains is essential for advancing the development of recommen…
UME: Upcycling Mixture-of-Experts for Scalable and Efficient Automatic Speech Recognition
Li Fu, Shanyong Yu, Siqi Li +3
Recent advancements in scaling up models have significantly improved performance in Automatic Speech Recognition (ASR) tasks. However, training large ASR models from scratch remain…
Accelerating Generative Recommendation via Simple Categorical User Sequence Compression
Qijiong Liu, Lu Fan, Zhongzhou Liu +7
Although generative recommenders demonstrate improved performance with longer sequences, their real-time deployment is hindered by substantial computational costs. To address this…
Neural2Speech: A Transfer Learning Framework for Neural-Driven Speech Reconstruction
Jiawei Li, Chunxu Guo, Li Fu +3
Reconstructing natural speech from neural activity is vital for enabling direct communication via brain-computer interfaces. Previous efforts have explored the conversion of neural…
Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New Benchmark
Li Xu, Bo Liu, Ameer Hamza Khan +2
With the availability of large-scale, comprehensive, and general-purpose vision-language (VL) datasets such as MSCOCO, vision-language pre-training (VLP) has become an active area…
Neighborhood-based Hard Negative Mining for Sequential Recommendation
Lu Fan, Jiashu Pu, Rongsheng Zhang +1
Negative sampling plays a crucial role in training successful sequential recommendation models. Instead of merely employing random negative sample selection, numerous strategies ha…
UFO2: A unified pre-training framework for online and offline speech recognition
Li Fu, Siqi Li, Qingtao Li +5
In this paper, we propose a Unified pre-training Framework for Online and Offline (UFO2) Automatic Speech Recognition (ASR), which 1) simplifies the two separate training workflows…
Can LLMs Outshine Conventional Recommenders? A Comparative Evaluation
Qijiong Liu, Jieming Zhu, Lu Fan +5
In recent years, integrating large language models (LLMs) into recommender systems has created new opportunities for improving recommendation quality. However, a comprehensive benc…
High-rate discrete-modulated continuous-variable quantum key distribution with composable security
Mingze Wu, Yan Pan, Junhui Li +8
Continuous-variable quantum key distribution holds the potential to generate high secret key rates, making it a prime candidate for high-rate metropolitan quantum network applicati…
Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training
Li-Ming Zhan, Haowen Liang, Bo Liu +3
Out-of-scope intent detection is of practical importance in task-oriented dialogue systems. Since the distribution of outlier utterances is arbitrary and unknown in the training st…
PolySpeech-100: A Large-Scale Benchmark for Speech Understanding Across 100+ Languages and Dialects
Sicheng Yang, Shulan Ruan, Shiwei Wu +4
While End-to-End (E2E) Speech-Large Language Models (Speech-LLMs) are rapidly evolving, their evaluation methodologies remain limited to the era of simple transcription. Existing b…
Learning Multi-Aspect Item Palette: A Semantic Tokenization Framework for Generative Recommendation
Qijiong Liu, Jieming Zhu, Zhaocheng Du +3
Traditional recommendation models often rely on unique item identifiers (IDs) to distinguish between items, which can hinder their ability to effectively leverage item content info…
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition
Li Fu, Xiaoxiao Li, Runyu Wang +5
End-to-end Automatic Speech Recognition (ASR) models are usually trained to optimize the loss of the whole token sequence, while neglecting explicit phonemic-granularity supervisio…
Do self-supervised speech and language models extract similar representations as human brain?
Peili Chen, Linyang He, Li Fu +3
Speech and language models trained through self-supervised learning (SSL) demonstrate strong alignment with brain activity during speech and language perception. However, given the…
Practical No-Switching Continuous-Variable Quantum Key Distribution with Biased Quadrature Detection
Jiale Mi, Yiming Bian, Lu Fan +2
Continuous-variable quantum key distribution protocol using coherent states and heterodyne detection, called No-Switching protocol, is widely used in practical systems due to the s…
Structural Compression for Phylogenetic Inference under Alignment Instability and Indel-Rich Evolution
Zhuoxin Zhang, Jieyu Wang, Fengyao Zhai +5
Phylogenetic inference traditionally relies on aligned characters under substitution models, but this framework becomes less reliable when alignments are unstable or when evolution…
PAC: Pronunciation-Aware Contextualized Large Language Model-based Automatic Speech Recognition
Li Fu, Yu Xin, Sunlu Zeng +3
This paper presents a Pronunciation-Aware Contextualized (PAC) framework to address two key challenges in Large Language Model (LLM)-based Automatic Speech Recognition (ASR) system…
High-rate discrete-modulated continuous-variable quantum key distribution with composable security
Mingze Wu, Yan Pan, Junhui Li +8
Continuous-variable quantum key distribution holds the potential to generate high secret key rates, making it a prime candidate for high-rate metropolitan quantum network applicati…
Quantum hacking against discrete-modulated continuous-variable quantum key distribution using modified local oscillator intensity attack with random fluctuations
Lu Fan, Yiming Bian, Mingze Wu +2
The local oscillator in practical continuous-variable quantum key distribution system fluctuates at any time during the key distribution process, which may open security loopholes…
N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network
Jinyin Chen, Yangyang Wu, Lu Fan +4
Recommender systems are becoming more and more important in our daily lives. However, traditional recommendation methods are challenged by data sparsity and efficiency, as the numb…
The Security Analysis of Continuous-Variable Quantum Key Distribution under Limited Eavesdropping with Practical Fiber
Sheng Liu, Lu Fan, Zhengyu Li +6
Research on optimal eavesdropping models under practical conditions will help to evaluate realistic risk when employing quantum key distribution (QKD) system for secure information…
LANID: LLM-assisted New Intent Discovery
Lu Fan, Jiashu Pu, Rongsheng Zhang +1
Task-oriented Dialogue Systems (TODS) often face the challenge of encountering new intents. New Intent Discovery (NID) is a crucial task that aims to identify these novel intents w…
Leveraging Label Information for Multimodal Emotion Recognition
Peiying Wang, Sunlu Zeng, Junqing Chen +4
Multimodal emotion recognition (MER) aims to detect the emotional status of a given expression by combining the speech and text information. Intuitively, label information should b…
Practical continuous-variable quantum key distribution using dynamic digital signal processing: security proof and experimental demonstration
Lu Fan, Zhengyu Li, Sheng Liu +8
Digital signal processing technology has paved the way for the realization of high-speed continuous-variable quantum key distribution systems. However, existing security proofs are…
OTF: Optimal Transport based Fusion of Supervised and Self-Supervised Learning Models for Automatic Speech Recognition
Li Fu, Siqi Li, Qingtao Li +6
Self-Supervised Learning (SSL) Automatic Speech Recognition (ASR) models have shown great promise over Supervised Learning (SL) ones in low-resource settings. However, the advantag…