60 citations · 124 across the 19 of their papers we have counts for
5 papers · 1 filter
Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging
Seong Jae Hwang, Zirui Tao, Won Hwa Kim +1
Generative models using neural network have opened a door to large-scale studies for various application domains, especially for studies that suffer from lack of real samples to ob…
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty
Hao Henry Zhou, Yunyang Xiong, Vikas Singh
We provide simple schemes to build Bayesian Neural Networks (BNNs), block by block, inspired by a recent idea of computation skeletons. We show how by adjusting the types of blocks…
A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices
Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen +5
In a number of disciplines, the data (e.g., graphs, manifolds) to be analyzed are non-Euclidean in nature. Geometric deep learning corresponds to techniques that generalize deep ne…
Sampling-free Uncertainty Estimation in Gated Recurrent Units with Exponential Families
Seong Jae Hwang, Ronak Mehta, Hyunwoo J. Kim +1
There has recently been a concerted effort to derive mechanisms in vision and machine learning systems to offer uncertainty estimates of the predictions they make. Clearly, there a…
Constrained Deep Learning using Conditional Gradient and Applications in Computer Vision
Sathya N. Ravi, Tuan Dinh, Vishnu Lokhande +1
A number of results have recently demonstrated the benefits of incorporating various constraints when training deep architectures in vision and machine learning. The advantages ran…