46 citations · 95 across the 6 of their papers we have counts for
6 papers
Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment
Yutong Xia, Yuxuan Liang, Haomin Wen +4
Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular method for STG…
AutoST: Training-free Neural Architecture Search for Spiking Transformers
Ziqing Wang, Qidong Zhao, Jinku Cui +2
Spiking Transformers have gained considerable attention because they achieve both the energy efficiency of Spiking Neural Networks (SNNs) and the high capacity of Transformers. How…
Deep Active Learning for Computer Vision: Past and Future
Rinyoichi Takezoe, Xu Liu, Shunan Mao +4
As an important data selection schema, active learning emerges as the essential component when iterating an Artificial Intelligence (AI) model. It becomes even more critical given…
Event-based Monocular Dense Depth Estimation with Recurrent Transformers
Xu Liu, Jianing Li, Xiaopeng Fan +1
Event cameras, offering high temporal resolutions and high dynamic ranges, have brought a new perspective to address common challenges (e.g., motion blur and low light) in monocula…
Task-Agnostic Learning to Accomplish New Tasks
Xianqi Zhang, Xingtao Wang, Xu Liu +3
Reinforcement Learning (RL) and Imitation Learning (IL) have made great progress in robotic decision-making in recent years. However, these methods show obvious deterioration for n…
The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems
Sixu Hu, Yuan Li, Xu Liu +3
This paper presents and characterizes an Open Application Repository for Federated Learning (OARF), a benchmark suite for federated machine learning systems. Previously available b…