Abstract Diffusion Probabilistic Models (DPMs) have recently demonstrated impressive
results on various generative tasks. Despite its promises, the learned representations of pre …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

Text-to-motion generation is a formidable task, aiming to produce human motions that align
with the input text while also adhering to human capabilities and physical laws. While there …

Diffusion Transformers have recently demonstrated unprecedented generative capabilities
for various tasks. The encouraging results, however, come with the cost of slow inference …

Conditional diffusion models serve as the foundation of modern image synthesis and find
extensive application in fields like computational biology and reinforcement learning. In …

Abstract Machine learning models face generalization challenges when exposed to out-of-
distribution (OOD) samples with unforeseen distribution shifts. Recent research reveals that …

Despite significant advancements in out-of-distribution (OOD) detection, existing methods
still struggle to maintain robustness against adversarial attacks, compromising their …

Abstract Machine learning-based algorithms demonstrate impressive performance across
numerous fields; however, they continue to suffer from certain limitations. Even sophisticated …

Recent developments in diffusion models have advanced conditioned image generation, yet
they struggle with reconstructing out-of-distribution (OOD) images, such as unseen tumors in …

Transformer-based methods have achieved remarkable success in various machine
learning tasks. How to design efficient test-time adaptation methods for transformer models …