all AI news
Integrating Symmetry into Differentiable Planning with Steerable Convolutions. (arXiv:2206.03674v2 [cs.LG] UPDATED)
Oct. 28, 2022, 1:12 a.m. | Linfeng Zhao, Xupeng Zhu, Lingzhi Kong, Robin Walters, Lawson L.S. Wong
cs.LG updates on arXiv.org arxiv.org
We study how group symmetry helps improve data efficiency and generalization
for end-to-end differentiable planning algorithms when symmetry appears in
decision-making tasks. Motivated by equivariant convolution networks, we treat
the path planning problem as \textit{signals} over grids. We show that value
iteration in this case is a linear equivariant operator, which is a (steerable)
convolution. This extends Value Iteration Networks (VINs) on using
convolutional networks for path planning with additional rotation and
reflection symmetry. Our implementation is based on VINs …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Principal Data Engineering Manager
@ Microsoft | Redmond, Washington, United States
Machine Learning Engineer
@ Apple | San Diego, California, United States