5 papers
Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks
Jurijs Nazarovs, Ronak R. Mehta, Vishnu Suresh Lokhande +1
Uncertainty estimation in deep models is essential in many real-world applications and has benefited from developments over the last several years. Recent evidence suggests that ex…
DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer
Haoliang Sun, Ronak Mehta, Hao H. Zhou +4
Positron emission tomography (PET) imaging is an imaging modality for diagnosing a number of neurological diseases. In contrast to Magnetic Resonance Imaging (MRI), PET is costly a…
Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?
Yunyang Xiong, Ronak Mehta, Vikas Singh
The design of neural network architectures is frequently either based on human expertise using trial/error and empirical feedback or tackled via large scale reinforcement learning…
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…
Finding Differentially Covarying Needles in a Temporally Evolving Haystack: A Scan Statistics Perspective
Ronak Mehta, Hyunwoo J. Kim, Shulei Wang +3
Recent results in coupled or temporal graphical models offer schemes for estimating the relationship structure between features when the data come from related (but distinct) longi…