3 citations · 3 across the 2 of their papers we have counts for
12 papers
A Decentralized Approach to Bayesian Learning
Anjaly Parayil, He Bai, Jemin George +1
Motivated by decentralized approaches to machine learning, we propose a collaborative Bayesian learning algorithm taking the form of decentralized Langevin dynamics in a non-convex…
Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models
Liam Hiley, Alun Preece, Yulia Hicks +3
A small subset of explainability techniques developed initially for image recognition models has recently been applied for interpretability of 3D Convolutional Neural Network model…
Sanity Checks for Saliency Metrics
Richard Tomsett, Dan Harborne, Supriyo Chakraborty +2
Saliency maps are a popular approach to creating post-hoc explanations of image classifier outputs. These methods produce estimates of the relevance of each pixel to the classifica…
Wasserstein Distance Based Domain Adaptation for Object Detection
Pengcheng Xu, Prudhvi Gurram, Gene Whipps +1
In this paper, we present an adversarial unsupervised domain adaptation framework for object detection. Prior approaches utilize adversarial training based on cross entropy between…
Distributed Deep Learning with Event-Triggered Communication
Jemin George, Prudhvi Gurram
We develop a Distributed Event-Triggered Stochastic GRAdient Descent (DETSGRAD) algorithm for solving non-convex optimization problems typically encountered in distributed deep lea…
Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning
Jemin George, Tao Yang, He Bai +1
We develop a distributed stochastic gradient descent algorithm for solving non-convex optimization problems under the assumption that the local objective functions are twice contin…