Physical Sciences and Mathematics Commons^™

Discrepancies Among Pre-Trained Deep Neural Networks: A New Threat To Model Zoo Reliability, Diego Montes, Pongpatapee Peerapatanapokin, Jeff Schultz, Chengjun Guo, Wenxin Jiang, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Training deep neural networks (DNNs) takes significant time and resources. A practice for expedited deployment is to use pre-trained deep neural networks (PTNNs), often from model zoos.collections of PTNNs; yet, the reliability of model zoos remains unexamined. In the absence of an industry standard for the implementation and performance of PTNNs, engineers cannot confidently incorporate them into production systems. As a first step, discovering potential discrepancies between PTNNs across model zoos would reveal a threat to model zoo reliability. Prior works indicated existing variances in deep learning systems in terms of accuracy. However, broader measures of reliability for PTNNs from …

The Fluid Representations Of Networks Estimating Liquid Viscosity, Jan Jaap R. Van Assen, Shin'ya Nishida, Roland W. Fleming

MODVIS Workshop

No abstract provided.

Learning From Minimally Labeled Data With Accelerated Convolutional Neural Networks, Aysegul Dundar

Open Access Dissertations

The main objective of an Artificial Vision Algorithm is to design a mapping function that takes an image as an input and correctly classifies it into one of the user-determined categories. There are several important properties to be satisfied by the mapping function for visual understanding. First, the function should produce good representations of the visual world, which will be able to recognize images independently of pose, scale and illumination. Furthermore, the designed artificial vision system has to learn these representations by itself. Recent studies on Convolutional Neural Networks (ConvNets) produced promising advancements in visual understanding. These networks attain significant …

On The 3d Point Cloud For Human-Pose Estimation, Kai-Chi Chan

Open Access Dissertations

This thesis aims at investigating methodologies for estimating a human pose from a 3D point cloud that is captured by a static depth sensor. Human-pose estimation (HPE) is important for a range of applications, such as human-robot interaction, healthcare, surveillance, and so forth. Yet, HPE is challenging because of the uncertainty in sensor measurements and the complexity of human poses. In this research, we focus on addressing challenges related to two crucial components in the estimation process, namely, human-pose feature extraction and human-pose modeling.

In feature extraction, the main challenge involves reducing feature ambiguity. We propose a 3D-point-cloud feature called …

Information Measures For Statistical Orbit Determination, Alinda Kenyana Mashiku

Open Access Dissertations

The current Situational Space Awareness (SSA) is faced with a huge task of tracking the increasing number of space objects. The tracking of space objects requires frequent and accurate monitoring for orbit maintenance and collision avoidance using methods for statistical orbit determination. Statistical orbit determination enables us to obtain estimates of the state and the statistical information of its region of uncertainty given by the probability density function (PDF). As even collision events with very low probability are important, accurate prediction of collisions require the representation of the full PDF of the random orbit state. Through representing the full PDF …