9 papers
SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices
Dongting Hu, Aarush Gupta, Magzhan Gabidolla +12
Recent advances in diffusion transformers (DiTs) have set new standards in image generation, yet remain impractical for on-device deployment due to their high computational and mem…
FedReLa: Imbalanced Federated Learning via Re-Labeling
Guangzheng Hu, Patricia Menéndez, Feng Liu +3
Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation. The global class imbalance and cross-client data heterogeneity n…
Are Two Datasets Close Enough With Statistical Significance? A Kernel Distributional Closeness Testing Approach
Zhijian Zhou, Liuhua Peng, Xunye Tian +2
Are two distributions close to each other with statistical significance? Distribution closeness testing (DCT) formalizes this question by testing whether the distance between a dis…
A Unified Data Representation Learning for Non-parametric Two-sample Testing
Xunye Tian, Liuhua Peng, Zhijian Zhou +3
Learning effective data representations has been crucial in non-parametric two-sample testing. Common approaches will first split data into training and test sets and then learn da…
LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning
Zhekai Du, Yinjie Min, Jingjing Li +5
Low-rank adaptation (LoRA) has become a prevalent method for adapting pre-trained large language models to downstream tasks. However, the simple low-rank decomposition form may con…
Effective transport by 2D turbulence: Vortex-gas theory vs. scale-invariant inverse cascade
Julie Meunier, Basile Gallet
The scale-invariant inverse energy cascade is a hallmark of 2D turbulence, with its theoretical energy spectrum observed in both direct numerical simulations (DNS) and laboratory e…