no code implementations • 29 Jan 2025 • Zijie Liu, Xinyu Zhao, Jie Peng, Zhuangdi Zhu, Qingyu Chen, Xia Hu, Tianlong Chen
Current medical AI systems often fail to replicate real-world clinical reasoning, as they are predominantly trained and evaluated on static text and question-answer tasks.
no code implementations • 21 Jun 2024 • Zhengbang Yang, Haotian Xia, Jingxi Li, Zezhi Chen, Zhuangdi Zhu, Weining Shen
Understanding sports is crucial for the advancement of Natural Language Processing (NLP) due to its intricate and dynamic nature.
no code implementations • 18 Jun 2024 • Haotian Xia, Zhengbang Yang, Yun Zhao, Yuqing Wang, Jingxi Li, Rhys Tracy, Zhuangdi Zhu, Yuan-Fang Wang, Hanjie Chen, Weining Shen
This survey provides a foundational resource for researchers and practitioners aiming to leverage NLP and multimodal models in sports, offering insights into current trends and future opportunities in the field.
no code implementations • 6 Feb 2023 • Jiajun Wu, Steve Drew, Fan Dong, Zhuangdi Zhu, Jiayu Zhou
In this paper, we conduct a comprehensive survey of the existing FL works focusing on network topologies.
no code implementations • 29 Sep 2021 • Boyang Liu, Zhuangdi Zhu, Pang-Ning Tan, Jiayu Zhou
We first discuss the limitations of directly using the noisy-label defense algorithms to defend against backdoor attacks.
1 code implementation • the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining 2021 • Junyuan Hong, Zhuangdi Zhu, Shuyang Yu, Zhangyang Wang, Hiroko Dodge, Jiayu Zhou
While adversarial learning is commonly used in centralized learning for mitigating bias, there are significant barriers when extending it to the federated framework.
4 code implementations • 20 May 2021 • Zhuangdi Zhu, Junyuan Hong, Jiayu Zhou
Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global server iteratively averages the model parameters of local users without accessing their data.
1 code implementation • NeurIPS 2020 • Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu Zhou
To further accelerate the learning procedure, we regulate the policy update with an inverse action model, which assists distribution matching from the perspective of mode-covering.
no code implementations • 16 Sep 2020 • Zhuangdi Zhu, Kaixiang Lin, Anil K. Jain, Jiayu Zhou
Reinforcement learning is a learning paradigm for solving sequential decision-making problems.
1 code implementation • 1 Apr 2020 • Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu Zhou
SAIL bridges the advantages of IL and RL to reduce the sample complexity substantially, by effectively exploiting sup-optimal demonstrations and efficiently exploring the environment to surpass the demonstrated performance.