26 papers
Efficient Test-Time Scaling for LLM-based Time Series Forecasting
Xuan-May Le, Minh-Tuan Tran, Ling Luo +3
Long-term time series forecasting benefits from preserving global structure such as trends and seasonality. Recent LLM-based forecasters often improve accuracy through test-time sc…
Hi-DREAM: Brain-Inspired Hierarchical Diffusion for fMRI-to-Image Reconstruction via ROI Encoder and VisuAl Mapping
Guowei Zhang, Yun Zhao, Kai Sun +4
Reconstructing natural images from fMRI requires bridging neural activity with both the structural and semantic representations used by modern generative models. Existing diffusion…
Adaptive Subspace Projection for Generative Personalization
Van-Anh Nguyen, Anh Tuan Bui, Tamas Abraham +5
Generative personalization often suffers from the semantic collapsing problem (SCP), where a learned personalized concept overpowers the rest of the text prompt, causing the model…
An Optimal Transport-driven Approach for Cultivating Latent Space in Online Incremental Learning
Quyen Tran, Hai Nguyen, Hoang Phan +6
In online incremental learning, data continuously arrives with substantial distributional shifts, creating a significant challenge because previous samples have limited replay valu…
Unveiling m-Sharpness Through the Structure of Stochastic Gradient Noise
Haocheng Luo, Mehrtash Harandi, Dinh Phung +1
Sharpness-aware minimization (SAM) has emerged as a highly effective technique to improve model generalization, but its underlying principles are not fully understood. We investiga…
Test-Time Instance-Specific Parameter Composition: A New Paradigm for Adaptive Generative Modeling
Minh-Tuan Tran, Xuan-May Le, Quan Hung Tran +3
Existing generative models, such as diffusion and auto-regressive networks, are inherently static, relying on a fixed set of pretrained parameters to handle all inputs. In contrast…