22 citations · 89 across the 36 of their papers we have counts for
11 papers · 1 filter
Visual CoT Makes VLMs Smarter but More Fragile
Chunxue Xu, Yiwei Wang, Yujun Cai +2
Chain-of-Thought (CoT) techniques have significantly enhanced reasoning in Vision-Language Models (VLMs). Extending this paradigm, Visual CoT integrates explicit visual edits, such…
Tricking Retrievers with Influential Tokens: An Efficient Black-Box Corpus Poisoning Attack
Cheng Wang, Yiwei Wang, Yujun Cai +1
Retrieval-augmented generation (RAG) systems enhance large language models by incorporating external knowledge, addressing issues like outdated internal knowledge and hallucination…
UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting
Juncheng Liu, Chenghao Liu, Gerald Woo +4
Transformer-based models have emerged as powerful tools for multivariate time series forecasting (MTSF). However, existing Transformer models often fall short of capturing both int…
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting
Xu Liu, Junfeng Hu, Yuan Li +4
Multivariate time series forecasting plays a pivotal role in contemporary web technologies. In contrast to conventional methods that involve creating dedicated models for specific…
LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting
Xu Liu, Yutong Xia, Yuxuan Liang +7
Road traffic forecasting plays a critical role in smart city initiatives and has experienced significant advancements thanks to the power of deep learning in capturing non-linear p…
Do We Really Need Graph Neural Networks for Traffic Forecasting?
Xu Liu, Yuxuan Liang, Chao Huang +4
Spatio-temporal graph neural networks (STGNN) have become the most popular solution to traffic forecasting. While successful, they rely on the message passing scheme of GNNs to est…
MGNNI: Multiscale Graph Neural Networks with Implicit Layers
Juncheng Liu, Bryan Hooi, Kenji Kawaguchi +1
Recently, implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs. In this paper, we introduce and justify two weaknesses o…
EIGNN: Efficient Infinite-Depth Graph Neural Networks
Juncheng Liu, Kenji Kawaguchi, Bryan Hooi +2
Graph neural networks (GNNs) are widely used for modelling graph-structured data in numerous applications. However, with their inherently finite aggregation layers, existing GNN mo…
Structure-Aware Label Smoothing for Graph Neural Networks
Yiwei Wang, Yujun Cai, Yuxuan Liang +5
Representing a label distribution as a one-hot vector is a common practice in training node classification models. However, the one-hot representation may not adequately reflect th…
When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?
Xu Liu, Yuxuan Liang, Chao Huang +3
Deep learning models are modern tools for spatio-temporal graph (STG) forecasting. Though successful, we argue that data scarcity is a key factor limiting their recent improvements…
LSCALE: Latent Space Clustering-Based Active Learning for Node Classification
Juncheng Liu, Yiwei Wang, Bryan Hooi +2
Node classification on graphs is an important task in many practical domains. It usually requires labels for training, which can be difficult or expensive to obtain in practice. Gi…