most citedNonlinear Sufficient Dimension Reduction with a Stochastic Neural Network

8 citations · 18 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2023

Bubble Explorer: Fast UAV Exploration in Large-Scale and Cluttered 3D-Environments using Occlusion-Free Spheres

Benxu Tang, Yunfan Ren, Fangcheng Zhu +4

Autonomous exploration is a crucial aspect of robotics that has numerous applications. Most of the existing methods greedily choose goals that maximize immediate reward. This strat…

cs.RO20235 cited

ROG-Map: An Efficient Robocentric Occupancy Grid Map for Large-scene and High-resolution LiDAR-based Motion Planning

Yunfan Ren, Yixi Cai, Fangcheng Zhu +2

Recent advances in LiDAR technology have opened up new possibilities for robotic navigation. Given the widespread use of occupancy grid maps (OGMs) in robotic motion planning, this…

stat.ME2022

A Double Regression Method for Graphical Modeling of High-dimensional Nonlinear and Non-Gaussian Data

Siqi Liang, Faming Liang

Graphical models have long been studied in statistics as a tool for inferring conditional independence relationships among a large set of random variables. The most existing works…

cs.LG20228 cited

Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network

Siqi Liang, Yan Sun, Faming Liang

Sufficient dimension reduction is a powerful tool to extract core information hidden in the high-dimensional data and has potentially many important applications in machine learnin…

stat.ML20225 cited

Interacting Contour Stochastic Gradient Langevin Dynamics

Wei Deng, Siqi Liang, Botao Hao +2

We propose an interacting contour stochastic gradient Langevin dynamics (ICSGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic gradient Langevin dynamics (C…