1 citations · 4 across the 16 of their papers we have counts for
16 papers
DIVER: A Robust Text-to-SQL System with Dynamic Interactive Value Linking and Evidence Reasoning
Yafeng Nan, Haifeng Sun, Zirui Zhuang +5
In the era of large language models, Text-to-SQL, as a natural language interface for databases, is playing an increasingly important role. The sota Text-to-SQL models have achieve…
ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Cross-Modal Alignment Dataset
Chengsen Wang, Qi Qi, Zhongwen Rao +3
Conventional forecasting methods are trained end-to-end on unimodal time series, which limits their ability to exploit textual information and undermines their generalization in da…
Unlocking the Potential of Linear Networks for Irregular Multivariate Time Series Forecasting
Chengsen Wang, Qi Qi, Jingyu Wang +3
Time series forecasting holds significant importance across various industries, including finance, transportation, energy, healthcare, and climate. Despite the widespread use of li…
MergeQuant: Accurate 4-bit Static Quantization of Large Language Models by Channel-wise Calibration
Jinguang Wang, Jingyu Wang, Haifeng Sun +6
Quantization has been widely used to compress and accelerate inference of large language models (LLMs). Existing methods focus on exploring the per-token dynamic calibration to ens…
Manboformer: Learning Gaussian Representations via Spatial-temporal Attention Mechanism
Ziyue Zhao, Qining Qi, Jianfa Ma
Compared with voxel-based grid prediction, in the field of 3D semantic occupation prediction for autonomous driving, GaussianFormer proposed using 3D Gaussian to describe scenes wi…
OIPR: Evaluation for Time-series Anomaly Detection Inspired by Operator Interest
Yuhan Jing, Jingyu Wang, Lei Zhang +6
With the growing adoption of time-series anomaly detection (TAD) technology, numerous studies have employed deep learning-based detectors to analyze time-series data in the fields…
ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data
Chengsen Wang, Qi Qi, Jingyu Wang +5
Human experts typically integrate numerical and textual multimodal information to analyze time series. However, most traditional deep learning predictors rely solely on unimodal nu…
Interdependency Matters: Graph Alignment for Multivariate Time Series Anomaly Detection
Yuanyi Wang, Haifeng Sun, Chengsen Wang +6
Anomaly detection in multivariate time series (MTS) is crucial for various applications in data mining and industry. Current industrial methods typically approach anomaly detection…
Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective
Chengsen Wang, Qi Qi, Jingyu Wang +4
Time series forecasting has played a pivotal role across various industries, including finance, transportation, energy, healthcare, and climate. Due to the abundant seasonal inform…
Multimedia Traffic Anomaly Detection
Tongtong Feng, Qi Qi, Jingyu Wang
Accuracy anomaly detection in user-level social multimedia traffic is crucial for privacy security. Compared with existing models that passively detect specific anomaly classes wit…
Deadline and Priority Constrained Immersive Video Streaming Transmission Scheduling
Tongtong Feng, Qi Qi, Bo He +1
Deadline-aware transmission scheduling in immersive video streaming is crucial. The objective is to guarantee that at least a certain block in multi-links is fully delivered within…
Meta-UAD: A Meta-Learning Scheme for User-level Network Traffic Anomaly Detection
Tongtong Feng, Qi Qi, Lingqi Guo +1
Accuracy anomaly detection in user-level network traffic is crucial for network security. Compared with existing models that passively detect specific anomaly classes with large la…
OutlierTune: Efficient Channel-Wise Quantization for Large Language Models
Jinguang Wang, Yuexi Yin, Haifeng Sun +5
Quantizing the activations of large language models (LLMs) has been a significant challenge due to the presence of structured outliers. Most existing methods focus on the per-token…
Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation
Yuanyi Wang, Wei Tang, Haifeng Sun +5
Weakly Supervised Entity Alignment (EA) is the task of identifying equivalent entities across diverse knowledge graphs (KGs) using only a limited number of seed alignments. Despite…
Towards Semantic Consistency: Dirichlet Energy Driven Robust Multi-Modal Entity Alignment
Yuanyi Wang, Haifeng Sun, Jiabo Wang +5
In Multi-Modal Knowledge Graphs (MMKGs), Multi-Modal Entity Alignment (MMEA) is crucial for identifying identical entities across diverse modal attributes. However, semantic incons…
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding
Yuanyi Wang, Haifeng Sun, Jingyu Wang +3
Entity alignment (EA), a pivotal process in integrating multi-source Knowledge Graphs (KGs), seeks to identify equivalent entity pairs across these graphs. Most existing approaches…