no code implementations • 22 May 2024 • Hanyu Zeng, Pengfei Zhou, Xin Lou, Zhen Wei Ng, David K. Y. Yau, Marianne Winslett
Different from existing approaches, the proposed framework does not rely on large amounts of well-curated labeled data but makes use of the massive unlabeled data in the wild which are easily accessible.
1 code implementation • 26 Oct 2021 • Prakhar Ganesh, Yao Chen, Yin Yang, Deming Chen, Marianne Winslett
Performance of object detection models has been growing rapidly on two major fronts, model accuracy and efficiency.
no code implementations • 27 Feb 2020 • Prakhar Ganesh, Yao Chen, Xin Lou, Mohammad Ali Khan, Yin Yang, Hassan Sajjad, Preslav Nakov, Deming Chen, Marianne Winslett
Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks.
no code implementations • 13 Oct 2019 • Zijian Li, Ruichu Cai, Kok Soon Chai, Hong Wei Ng, Hoang Dung Vu, Marianne Winslett, Tom Z. J. Fu, Boyan Xu, Xiaoyan Yang, Zhenjie Zhang
However, the mainstream domain adaptation methods cannot achieve ideal performance on time series data, because most of them focus on static samples and even the existing time series domain adaptation methods ignore the properties of time series data, such as temporal causal mechanism.