no code implementations • 4 Mar 2023 • Alex Fu, Lingjie Kong
Inspired by the recent success of application of dense data approach by using ORB-SLAM and RGB-D SLAM, we propose a better pipeline of real-time SLAM in dynamics environment.
no code implementations • 4 Mar 2023 • Yangxin Zhong, Jiajie He, Lingjie Kong
Reinforcement Learning (RL) is an area of machine learning figuring out how agents take actions in an unknown environment to maximize its rewards.
1 code implementation • 26 Feb 2023 • Lingjie Kong, Pankaj Rajak, Siamak Shakeri
Point clouds are rich geometric data structures, where their three dimensional structure offers an excellent domain for understanding the representation learning and generative modeling in 3D space.
no code implementations • 26 Feb 2023 • Shenli Yuan, Lingjie Kong, Jiushuang Guo
In this paper, we introduced our new neural network architecture built on top of the current state-of-the-art Onsets and Frames, and compared the performances of its multiple variations on AMT task.
no code implementations • 26 Feb 2023 • Lingjie Kong, Yun Liao
In the initial PAN paper, it uses a path integral based graph neural networks for graph prediction.
no code implementations • CVPR 2023 • Xiang Li, Xuelin Qian, Litian Liang, Lingjie Kong, Qiaole Dong, Jiejun Chen, Dingxia Liu, Xiuzhong Yao, Yanwei Fu
Particularly, we build a causal graph, and train the images to estimate the intraoperative attributes for final OS prediction.
1 code implementation • 17 May 2022 • Woong Gyu La, Lingjie Kong, Sunil Muralidhara, Pratik Nichat
We propose DeepSim, a reinforcement learning environment build toolkit for ROS and Gazebo.
1 code implementation • 14 May 2022 • Woong Gyu La, Sunil Muralidhara, Lingjie Kong, Pratik Nichat
With environment virtualization and its interface design, the agent policies can be trained in multiple machines for a multi-agent environment.