48 citations · 288 across the 47 of their papers we have counts for
8 papers · 1 filter
Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery
Yiqin Yang, Hao Hu, Wenzhe Li +4
Offline reinforcement learning (RL) enables the agent to effectively learn from logged data, which significantly extends the applicability of RL algorithms in real-world scenarios…
SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning
Cheng Tan, Zhangyang Gao, Siyuan Li +1
Recent years have witnessed remarkable advances in spatiotemporal predictive learning, with methods incorporating auxiliary inputs, complex neural architectures, and sophisticated…
MogaNet: Multi-order Gated Aggregation Network
Siyuan Li, Zedong Wang, Zicheng Liu +6
By contextualizing the kernel as global as possible, Modern ConvNets have shown great potential in computer vision tasks. However, recent progress on multi-order game-theoretic int…
Leveraging Graph-based Cross-modal Information Fusion for Neural Sign Language Translation
Jiangbin Zheng, Siyuan Li, Cheng Tan +3
Sign Language (SL), as the mother tongue of the deaf community, is a special visual language that most hearing people cannot understand. In recent years, neural Sign Language Trans…
CUP: Critic-Guided Policy Reuse
Jin Zhang, Siyuan Li, Chongjie Zhang
The ability to reuse previous policies is an important aspect of human intelligence. To achieve efficient policy reuse, a Deep Reinforcement Learning (DRL) agent needs to decide wh…
Are Gradients on Graph Structure Reliable in Gray-box Attacks?
Zihan Liu, Yun Luo, Lirong Wu +3
Graph edge perturbations are dedicated to damaging the prediction of graph neural networks by modifying the graph structure. Previous gray-box attackers employ gradients from the s…
Hyperspherical Consistency Regularization
Cheng Tan, Zhangyang Gao, Lirong Wu +2
Recent advances in contrastive learning have enlightened diverse applications across various semi-supervised fields. Jointly training supervised learning and unsupervised learning…
Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining
Di Wu, Siyuan Li, Jie Yang +1
Deep learning associated with neurological signals is poised to drive major advancements in diverse fields such as medical diagnostics, neurorehabilitation, and brain-computer inte…