1 citations · 2 across the 5 of their papers we have counts for
5 papers
A Particle-based Sparse Gaussian Process Optimizer
Chandrajit Bajaj, Omatharv Bharat Vaidya, Yi Wang
Task learning in neural networks typically requires finding a globally optimal minimizer to a loss function objective. Conventional designs of swarm based optimization methods appl…
E-CIR: Event-Enhanced Continuous Intensity Recovery
Chen Song, Qixing Huang, Chandrajit Bajaj
A camera begins to sense light the moment we press the shutter button. During the exposure interval, relative motion between the scene and the camera causes motion blur, a common u…
ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators
Qixing Huang, Xiangru Huang, Bo Sun +3
This paper introduces an unsupervised loss for training parametric deformation shape generators. The key idea is to enforce the preservation of local rigidity among the generated s…
Scene Synthesis via Uncertainty-Driven Attribute Synchronization
Haitao Yang, Zaiwei Zhang, Siming Yan +5
Developing deep neural networks to generate 3D scenes is a fundamental problem in neural synthesis with immediate applications in architectural CAD, computer graphics, as well as i…
Deep Predictive Learning of Carotid Stenosis Severity
Yiqun Diao, Oliver Zhao, Priya Kothapalli +2
Carotid artery stenosis is the narrowing of carotid arteries, which supplies blood to the neck and head. In this work, we train a model to predict the severity of the stenosis bloc…