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Found 8 papers, 3 papers with code
Real-time SLAM Pipeline in Dynamics Environment
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.
Double A3C: Deep Reinforcement Learning on OpenAI Gym Games
Reinforcement Learning (RL) is an area of machine learning figuring out how agents take actions in an unknown environment to maximize its rewards.
Generative Models for 3D Point Clouds
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.
From Audio to Symbolic Encoding
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.
Path Integral Based Convolution and Pooling for Heterogeneous Graph Neural Networks
In the initial PAN paper, it uses a path integral based graph neural networks for graph prediction.
Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction
Particularly, we build a causal graph, and train the images to estimate the intraoperative attributes for final OS prediction.
DeepSim: A Reinforcement Learning Environment Build Toolkit for ROS and Gazebo
We propose DeepSim, a reinforcement learning environment build toolkit for ROS and Gazebo.
Unified Distributed Environment
With environment virtualization and its interface design, the agent policies can be trained in multiple machines for a multi-agent environment.
