14 citations · 31 across the 10 of their papers we have counts for
13 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…
A Bayesian Theory of Change Detection in Statistically Periodic Random Processes
Taposh Banerjee, Prudhvi Gurram, Gene Whipps
A new class of stochastic processes called independent and periodically identically distributed (i.p.i.d.) processes is defined to capture periodically varying statistical behavior…
Minimax-Optimal Algorithms for Detecting Changes in Statistically Periodic Random Processes
Taposh Banerjee, Prudhvi Gurram, Gene Whipps
Theory and algorithms are developed for detecting changes in the distribution of statistically periodic random processes. The statistical periodicity is modeled using independent a…
Quickest Detection Of Deviations From Periodic Statistical Behavior
Taposh Banerjee, Prudhvi Gurram, Gene Whipps
A new class of stochastic processes called independent and periodically identically distributed (i.p.i.d.) processes is defined to capture periodically varying statistical behavior…
Cyclostationary Statistical Models and Algorithms for Anomaly Detection Using Multi-Modal Data
Taposh Banerjee, Gene Whipps, Prudhvi Gurram +1
A framework is proposed to detect anomalies in multi-modal data. A deep neural network-based object detector is employed to extract counts of objects and sub-events from the data.…
Sequential Event Detection Using Multimodal Data in Nonstationary Environments
Taposh Banerjee, Gene Whipps, Prudhvi Gurram +1
The problem of sequential detection of anomalies in multimodal data is considered. The objective is to observe physical sensor data from CCTV cameras, and social media data from Tw…
Human Action Attribute Learning From Video Data Using Low-Rank Representations
Tong Wu, Prudhvi Gurram, Raghuveer M. Rao +1
Representation of human actions as a sequence of human body movements or action attributes enables the development of models for human activity recognition and summarization. We pr…
Optimal Sparse Kernel Learning for Hyperspectral Anomaly Detection
Zhimin Peng, Prudhvi Gurram, Heesung Kwon +1
In this paper, a novel framework of sparse kernel learning for Support Vector Data Description (SVDD) based anomaly detection is presented. In this work, optimal sparse feature sel…