Chenghao Liu

Bridging the gap between pretrained time series models and reliable deployment could redefine how we approach time series analysis in diverse applications.

Achieving a balance between pattern preservation and attribute retrieval, this method boosts retrieval performance while maintaining fine-grained contextual integrity.

Foundation models may excel at forecasting, but their accuracy doesn't always translate to better resource allocation decisions in cloud environments.

MOSS-Audio achieves state-of-the-art performance in audio understanding tasks by effectively integrating temporal cues and deep acoustic features, setting a new benchmark for audio-language models.

VLAs learn to predict task success even when trained only with imitation learning, opening the door to improved performance without expensive reward engineering.