no code implementations • 28 Mar 2023 • Yuanhao Xiong, Long Zhao, Boqing Gong, Ming-Hsuan Yang, Florian Schroff, Ting Liu, Cho-Jui Hsieh, Liangzhe Yuan
Existing video-language pre-training methods primarily focus on instance-level alignment between video clips and captions via global contrastive learning but neglect rich fine-grained local information in both videos and text, which is of importance to downstream tasks requiring temporal localization and semantic reasoning.
1 code implementation • 27 Sep 2022 • Ruochen Wang, Yuanhao Xiong, Minhao Cheng, Cho-Jui Hsieh
Efficient and automated design of optimizers plays a crucial role in full-stack AutoML systems.
1 code implementation • CVPR 2023 • Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, Cho-Jui Hsieh
Federated learning~(FL) has recently attracted increasing attention from academia and industry, with the ultimate goal of achieving collaborative training under privacy and communication constraints.
1 code implementation • NAACL 2022 • Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit Dhillon
To learn the semantic embeddings of instances and labels with raw text, we propose to pre-train Transformer-based encoders with self-supervised contrastive losses.
Multi Label Text Classification Multi-Label Text Classification +2
no code implementations • 13 Oct 2021 • Yunxiao Qin, Yuanhao Xiong, JinFeng Yi, Lihong Cao, Cho-Jui Hsieh
In this paper, we define a Generalized Transferable Attack (GTA) problem where the attacker doesn't know this information and is acquired to attack any randomly encountered images that may come from unknown datasets.
no code implementations • ICLR 2022 • Yuanhao Xiong, Li-Cheng Lan, Xiangning Chen, Ruochen Wang, Cho-Jui Hsieh
By constructing a directed graph for the underlying neural network of the target problem, GNS encodes current dynamics with a graph message passing network and trains an agent to control the learning rate accordingly via reinforcement learning.
2 code implementations • 5 Sep 2021 • Yunxiao Qin, Yuanhao Xiong, JinFeng Yi, Cho-Jui Hsieh
In this paper, we tackle this problem from a novel angle -- instead of using the original surrogate models, can we obtain a Meta-Surrogate Model (MSM) such that attacks to this model can be easier transferred to other models?
no code implementations • 1 Jan 2021 • Yuanhao Xiong, Cho-Jui Hsieh
Recent decades have witnessed great prosperity of deep learning in tackling various problems such as classification and decision making.
no code implementations • 19 Oct 2020 • Yuanhao Xiong, Xuanqing Liu, Li-Cheng Lan, Yang You, Si Si, Cho-Jui Hsieh
For end-to-end efficiency, unlike previous work that assumes random hyperparameter tuning, which over-emphasizes the tuning time, we propose to evaluate with a bandit hyperparameter tuning strategy.
no code implementations • ECCV 2020 • Yuanhao Xiong, Cho-Jui Hsieh
Adversarial attack has recently become a tremendous threat to deep learning models.
1 code implementation • ICLR 2020 • Yangjun Ruan, Yuanhao Xiong, Sashank Reddi, Sanjiv Kumar, Cho-Jui Hsieh
In the learning to learn (L2L) framework, we cast the design of optimization algorithms as a machine learning problem and use deep neural networks to learn the update rules.
1 code implementation • 12 May 2019 • Guanjie Zheng, Yuanhao Xiong, Xinshi Zang, Jie Feng, Hua Wei, Huichu Zhang, Yong Li, Kai Xu, Zhenhui Li
Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions.
5 code implementations • ICLR 2019 • Liangchen Luo, Yuanhao Xiong, Yan Liu, Xu sun
Recent work has put forward some algorithms such as AMSGrad to tackle this issue but they failed to achieve considerable improvement over existing methods.