5 papers · 1 filter
Partial GFlowNet: Accelerating Convergence in Large State Spaces via Strategic Partitioning
Xuan Yu, Xu Wang, Rui Zhu +2
Generative Flow Networks (GFlowNets) have shown promising potential to generate high-scoring candidates with probability proportional to their rewards. As existing GFlowNets freely…
Exploring Multiple High-Scoring Subspaces in Generative Flow Networks
Xuan Yu, Xu Wang, Rui Zhu +2
As a probabilistic sampling framework, Generative Flow Networks (GFlowNets) show strong potential for constructing complex combinatorial objects through the sequential composition…
Planning-Augmented Sampling with Early Guidance for High-Reward Discovery
Rui Zhu, Yudong Zhang, Xuan Yu +3
Generative Flow Networks (GFlowNets) enable structured generation with inherent diversity, but existing sampling strategies often rely on weak guided exploration, slowing early dis…
A General ReLearner: Empowering Spatiotemporal Prediction by Re-learning Input-label Residual
Jiaming Ma, Binwu Wang, Pengkun Wang +3
Prevailing spatiotemporal prediction models typically operate under a forward (unidirectional) learning paradigm, in which models extract spatiotemporal features from historical ob…
Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework
Zhongchao Yi, Zhengyang Zhou, Qihe Huang +4
Spatiotemporal learning has become a pivotal technique to enable urban intelligence. Traditional spatiotemporal models mostly focus on a specific task by assuming a same distributi…