24 citations · 63 across the 4 of their papers we have counts for
5 papers
Learning Gradient Fields for Shape Generation
Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor +4
In this work, we propose a novel technique to generate shapes from point cloud data. A point cloud can be viewed as samples from a distribution of 3D points whose density is concen…
QPyTorch: A Low-Precision Arithmetic Simulation Framework
Tianyi Zhang, Zhiqiu Lin, Guandao Yang +1
Low-precision training reduces computational cost and produces efficient models. Recent research in developing new low-precision training algorithms often relies on simulation to e…
PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows
Guandao Yang, Xun Huang, Zekun Hao +3
As 3D point clouds become the representation of choice for multiple vision and graphics applications, the ability to synthesize or reconstruct high-resolution, high-fidelity point…
SWALP : Stochastic Weight Averaging in Low-Precision Training
Guandao Yang, Tianyi Zhang, Polina Kirichenko +3
Low precision operations can provide scalability, memory savings, portability, and energy efficiency. This paper proposes SWALP, an approach to low precision training that averages…
Fast Reading Comprehension with ConvNets
Felix Wu, Ni Lao, John Blitzer +2
State-of-the-art deep reading comprehension models are dominated by recurrent neural nets. Their sequential nature is a natural fit for language, but it also precludes parallelizat…