7 citations · 17 across the 9 of their papers we have counts for
3 papers · 1 filter
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…
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks
Adam Li, Ronan Perry, Chester Huynh +6
Decision forests (Forests), in particular random forests and gradient boosting trees, have demonstrated state-of-the-art accuracy compared to other methods in many supervised learn…
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…