Search Results for author: Chenhao Li

Found 8 papers, 2 papers with code

FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning

no code implementations21 Feb 2024 Chenhao Li, Elijah Stanger-Jones, Steve Heim, Sangbae Kim

Motion trajectories offer reliable references for physics-based motion learning but suffer from sparsity, particularly in regions that lack sufficient data coverage.

GenoCraft: A Comprehensive, User-Friendly Web-Based Platform for High-Throughput Omics Data Analysis and Visualization

1 code implementation21 Dec 2023 Yingzhou Lu, Minjie Shen, Yue Zhao, Chenhao Li, Fan Meng, Xiao Wang, David Herrington, Yue Wang, Tim Fu, Capucine van Rechem

With GenoCraft, researchers and data scientists have access to an array of cutting-edge bioinformatics tools under a user-friendly interface, making it a valuable resource for managing and analyzing large-scale omics data.

NeISF: Neural Incident Stokes Field for Geometry and Material Estimation

no code implementations22 Nov 2023 Chenhao Li, Taishi Ono, Takeshi Uemori, Hajime Mihara, Alexander Gatto, Hajime Nagahara, Yusuke Moriuchi

To address this problem, we propose Neural Incident Stokes Fields (NeISF), a multi-view inverse rendering framework that reduces ambiguities using polarization cues.

Inverse Rendering

Learning Diverse Skills for Local Navigation under Multi-constraint Optimality

no code implementations3 Oct 2023 Jin Cheng, Marin Vlastelica, Pavel Kolev, Chenhao Li, Georg Martius

We demonstrate the effectiveness of our method on a local navigation task where a quadruped robot needs to reach the target within a finite horizon.

Efficient and Secure Federated Learning for Financial Applications

no code implementations15 Mar 2023 Tao Liu, Zhi Wang, Hui He, Liangliang Lin, Wei Shi, Ran An, Chenhao Li

Experiments show that under different Non-IID experiment settings, our method can reduce the upload communication cost to about 2. 9% to 18. 9% of the conventional federated learning algorithm when the sparse rate is 0. 01.

Federated Learning

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