no code implementations • 22 Mar 2024 • Haoyu Wang, Xiaoyu Tan, Xihe Qiu, Chao Qu
Effective coordination is crucial for motion control with reinforcement learning, especially as the complexity of agents and their motions increases.
no code implementations • 9 Dec 2023 • Zhenting Qi, Xiaoyu Tan, Shaojie Shi, Chao Qu, Yinghui Xu, Yuan Qi
Instruction fine-tuning has conventionally been employed to adapt Large Language Models (LLMs) to a variety of tasks.
1 code implementation • 27 Sep 2023 • Weidi Xu, Jingwei Wang, Lele Xie, Jianshan He, Hongting Zhou, Taifeng Wang, Xiaopei Wan, Jingdong Chen, Chao Qu, Wei Chu
Integrating first-order logic constraints (FOLCs) with neural networks is a crucial but challenging problem since it involves modeling intricate correlations to satisfy the constraints.
1 code implementation • ICCV 2023 • Xihe Qiu, Shaojie Shi, Xiaoyu Tan, Chao Qu, Zhijun Fang, Hailing Wang, Yongbin Gao, Peixia Wu, Huawei Li
Video nystagmography (VNG) is the diagnostic gold standard of benign paroxysmal positional vertigo (BPPV), which requires medical professionals to examine the direction, frequency, intensity, duration, and variation in the strength of nystagmus on a VNG video.
1 code implementation • 31 May 2022 • Siqiao Xue, Chao Qu, Xiaoming Shi, Cong Liao, Shiyi Zhu, Xiaoyu Tan, Lintao Ma, Shiyu Wang, Shijun Wang, Yun Hu, Lei Lei, Yangfei Zheng, Jianguo Li, James Zhang
Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud.
1 code implementation • 29 Jan 2022 • Chao Qu, Xiaoyu Tan, Siqiao Xue, Xiaoming Shi, James Zhang, Hongyuan Mei
We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such that its long-term reward is maximized.
2 code implementations • 14 Sep 2021 • Xu Liu, Guilherme V. Nardari, Fernando Cladera Ojeda, Yuezhan Tao, Alex Zhou, Thomas Donnelly, Chao Qu, Steven W. Chen, Roseli A. F. Romero, Camillo J. Taylor, Vijay Kumar
Semantic maps represent the environment using a set of semantically meaningful objects.
no code implementations • ICCV 2021 • Chao Qu, Wenxin Liu, Camillo J. Taylor
By adopting a Bayesian treatment, our Bayesian Deep Basis Fitting (BDBF) approach is able to 1) predict high-quality uncertainty estimates and 2) enable depth completion with few or no sparse measurements.
no code implementations • 16 Jun 2020 • Xiaoyu Tan, Chao Qu, Junwu Xiong, James Zhang
Model-based reinforcement learning (MBRL) has shown its advantages in sample-efficiency over model-free reinforcement learning (MFRL).
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 19 Apr 2020 • Chao Qu, Hui Li, Chang Liu, Junwu Xiong, James Zhang, Wei Chu, Weiqiang Wang, Yuan Qi, Le Song
We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions over agents.
Multi-agent Reinforcement Learning reinforcement-learning +2
no code implementations • 29 Dec 2019 • Steven W. Chen, Guilherme V. Nardari, Elijah S. Lee, Chao Qu, Xu Liu, Roseli A. F. Romero, Vijay Kumar
This paper describes an end-to-end pipeline for tree diameter estimation based on semantic segmentation and lidar odometry and mapping.
no code implementations • 21 Dec 2019 • Chao Qu, Ty Nguyen, Camillo J. Taylor
In this paper we consider the task of image-guided depth completion where our system must infer the depth at every pixel of an input image based on the image content and a sparse set of depth measurements.
no code implementations • 25 Sep 2019 • Xiaoyu Tan, Chao Qu, Junwu Xiong, James Zhang
In this paper, we propose a simple and elegant model-based reinforcement learning algorithm called soft stochastic value gradient method (S2VG).
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • NeurIPS 2019 • Chao Qu, Shie Mannor, Huan Xu, Yuan Qi, Le Song, Junwu Xiong
To the best of our knowledge, it is the first MARL algorithm with convergence guarantee in the control, off-policy and non-linear function approximation setting.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 20 May 2018 • Chao Qu, Shie Mannor, Huan Xu
We devise a distributional variant of gradient temporal-difference (TD) learning.
no code implementations • 20 May 2018 • Yan Li, Chao Qu, Huan Xu
We demonstrate this advantage and show that the linear oracle complexity can be reduced to almost the same order of magnitude as the communication complexity, when the feasible set is polyhedral.
no code implementations • 20 May 2018 • Yan Li, Chao Qu, Huan Xu
Recently people have reduced the gradient evaluation complexity of FW algorithm to $\log(\frac{1}{\epsilon})$ for the smooth and strongly convex objective.
no code implementations • 1 Apr 2018 • Xu Liu, Steven W. Chen, Shreyas Aditya, Nivedha Sivakumar, Sandeep Dcunha, Chao Qu, Camillo J. Taylor, Jnaneshwar Das, Vijay Kumar
We present a novel fruit counting pipeline that combines deep segmentation, frame to frame tracking, and 3D localization to accurately count visible fruits across a sequence of images.
no code implementations • 13 Feb 2018 • Chao Qu, Yan Li, Huan Xu
While optimizing convex objective (loss) functions has been a powerhouse for machine learning for at least two decades, non-convex loss functions have attracted fast growing interests recently, due to many desirable properties such as superior robustness and classification accuracy, compared with their convex counterparts.
no code implementations • 6 Dec 2017 • Kartik Mohta, Michael Watterson, Yash Mulgaonkar, Sikang Liu, Chao Qu, Anurag Makineni, Kelsey Saulnier, Ke Sun, Alex Zhu, Jeffrey Delmerico, Konstantinos Karydis, Nikolay Atanasov, Giuseppe Loianno, Davide Scaramuzza, Kostas Daniilidis, Camillo Jose Taylor, Vijay Kumar
One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment.
Robotics
no code implementations • 19 Feb 2017 • Chao Qu, Yan Li, Huan Xu
SAGA is a fast incremental gradient method on the finite sum problem and its effectiveness has been tested on a vast of applications.
no code implementations • 26 Jan 2017 • Chao Qu, Huan Xu
In this paper, we consider stochastic dual coordinate (SDCA) {\em without} strongly convex assumption or convex assumption.
no code implementations • 7 Nov 2016 • Chao Qu, Yan Li, Huan Xu
SVRG and its variants are among the state of art optimization algorithms for large scale machine learning problems.
no code implementations • NeurIPS 2015 • Chao Qu, Huan Xu
This paper considers the subspace clustering problem where the data contains irrelevant or corrupted features.