19 citations · 70 across the 32 of their papers we have counts for
9 papers · 1 filter
Learning Spatio-Temporal Foundation Models from Pure Synthetic Data
Yutong Feng, Shiyuan Piao, Yutong Xia +5
Spatio-Temporal Foundation Models (STFMs) aim to learn generalizable representations of complex dynamical systems across space and time. However, existing approaches suffer from di…
DeMa: Dual-Path Delay-Aware Mamba for Efficient Multivariate Time Series Analysis
Rui An, Haohao Qu, Wenqi Fan +2
Accurate and efficient multivariate time series (MTS) analysis is increasingly critical for a wide range of intelligent applications. Within this realm, Transformers have emerged a…
Damba-ST: Domain-Adaptive Mamba for Efficient Urban Spatio-Temporal Prediction
Rui An, Yifeng Zhang, Ziran Liang +4
Training urban spatio-temporal foundation models that generalize well across diverse regions and cities is critical for deploying urban services in unseen or data-scarce regions. R…
GLProtein: Global-and-Local Structure Aware Protein Representation Learning
Yunqing Liu, Wenqi Fan, Xiaoyong Wei +1
Proteins are central to biological systems, participating as building blocks across all forms of life. Despite advancements in understanding protein functions through protein seque…
SemDiff: Generating Natural Unrestricted Adversarial Examples via Semantic Attributes Optimization in Diffusion Models
Zeyu Dai, Shengcai Liu, Rui He +5
Unrestricted adversarial examples (UAEs), allow the attacker to create non-constrained adversarial examples without given clean samples, posing a severe threat to the safety of dee…
iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network
Ziran Liang, Rui An, Wenqi Fan +2
As time evolves, data within specific domains exhibit predictability that motivates time series forecasting to predict future trends from historical data. However, current deep for…
A Survey of Mamba
Haohao Qu, Liangbo Ning, Rui An +5
As one of the most representative DL techniques, Transformer architecture has empowered numerous advanced models, especially the large language models (LLMs) that comprise billions…
Backdoor Graph Condensation
Jiahao Wu, Ning Lu, Zeiyu Dai +5
Graph condensation has recently emerged as a prevalent technique to improve the training efficiency for graph neural networks (GNNs). It condenses a large graph into a small one su…
Graph Machine Learning in the Era of Large Language Models (LLMs)
Shijie Wang, Jiani Huang, Zhikai Chen +8
Graphs play an important role in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. With the advent of deep lear…