Dataset distillation (DD) condenses a large original dataset into a small distilled dataset with high training utility. Decoupled statistical matching methods substantially reduce distillation time and memory overhead while achieving strong performance. However, they typically supervise all distilled samples using runn...
Hong-Xu Ma, Guang Li, Shi-Jie Wang et al.· 0 citations
Precipitation nowcasting, generating future radar fields from past observations, is critical for flood warning and disaster response. It is also a demanding benchmark for spatiotemporal generative modeling, with chaotic dynamics, heavy-tailed intensities, and rare high-intensity structures that matter most. Determinist...
Shunya Nagashima, Takumi Bannai, Makoto Misaizu et al.· 0 citations
ForUM is presented, a training-free test-time fusion of frozen MLLMs guided by two fixed geometric rules: agreement-based selection keeps the region supported by the most distinct models, and medoid localization returns an actual member box instead of a coordinate average, so one loose prediction cannot shift the answe...
Taiyo Sato, Takamasa Sanda, Keisuke Maeda et al.· 0 citations
This study aims to accurately predict students’ academic performance trajectories for university entrance examinations by proposing a machine learning framework that explicitly accounts for missing data and uncertainty. Mock examination data are characterized by substantial missing values due to heterogeneous participa...
DeCO uses attention rollout from a pretrained TransFG teacher to identify informative patches, applies spatial diversification to reduce redundant coverage, and organizes the resulting regions into class-wise evidence banks and consistently outperforms representative coreset and dataset-distillation baselines under dif...
Chuixuan Fan, Guang Li, Shi-Jie Wang et al.· 0 citations
Self-supervised representation-guided generative dataset distillation (SRG) is proposed, a framework that translates the SSL geometry into diffusion guidance and consistently outperforms the evaluated generative baselines across multiple datasets and IPC settings.
Mingzhuo Li, Guang Li, Linfeng Ye et al.· 0 citations
A controlled SASRec variant is studied that replaces learned attention weights with uniform aggregation and is trained under an otherwise identical block structure and training recipe, and this modification often yields performance comparable to the original model, with clear dataset-dependent exceptions.
K.Ozaki K.Ozaki, Keigo Sakurai, Ren Togo et al.· Annual International ACM SIG...· 0 citations
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