ML for Semiconductor Bond Pad Defect Detection
Abstract not provided.
Publications
Machine Learning with Synthetic Data: What Approaches are Most Effective?
Abstract not provided.
Big data actionable intelligence architecture
Journal of Big Data
The amount of data produced by sensors, social and digital media, and Internet of Things (IoTs) are rapidly increasing each day. Decision makers often need to sift through a sea of Big Data to utilize information from a variety of sources in order to determine a course of action. This can be a very difficult and time-consuming task. For each data source encountered, the information can be redundant, conflicting, and/or incomplete. For near-real-time application, there is insufficient time for a human to interpret all the information from different sources. In this project, we have developed a near-real-time, data-agnostic, software architecture that is capable of using several disparate sources to autonomously generate Actionable Intelligence with a human in the loop. We demonstrated our solution through a traffic prediction exemplar problem.
Semisupervised learning for seismic monitoring applications
Seismological Research Letters
The impressive performance that deep neural networks demonstrate on a range of seismic monitoring tasks depends largely on the availability of event catalogs that have been manually curated over many years or decades. However, the quality, duration, and availability of seismic event catalogs vary significantly across the range of monitoring operations, regions, and objectives. Semisupervised learning (SSL) enables learning from both labeled and unlabeled data and provides a framework to leverage the abundance of unreviewed seismic data for training deep neural networks on a variety of target tasks. We apply two SSL algorithms (mean-teacher and virtual adversarial training) as well as a novel hybrid technique (exponential average adversarial training) to seismic event classification to examine how unlabeled data with SSL can enhance model performance. In general, we find that SSL can perform as well as supervised learning with fewer labels. We also observe in some scenarios that almost half of the benefits of SSL are the result of the meaningful regularization enforced through SSL techniques and may not be attributable to unlabeled data directly. Lastly, the benefits from unlabeled data scale with the difficulty of the predictive task when we evaluate the use of unlabeled data to characterize sources in new geographic regions. In geographic areas where supervised model performance is low, SSL significantly increases the accuracy of source-type classification using unlabeled data.
RF Feature Extraction using Wireless Protocol Informatics Pattern Matching and Natural Language Processing
Abstract not provided.
Semi-Supervised Learning for Seismic Monitoring Applications
Abstract not provided.
Seismic Phase Identification with a Merged Deep Neural Net
Abstract not provided.
Posters for AA/CE Reception
Abstract not provided.
Seismic Phase Identification with Speech Recognition Algorithms
Abstract not provided.
Seismic Phase Identification with a Merged Deep Neural Network
Abstract not provided.
Semi-Supervised Classification of Seismic Events in an Inaccessible Region
Abstract not provided.
Applying Natural Language Processing (NLP) to RF Signal Patterns
Abstract not provided.
SasquatchDeepCloud Image Classification Training Application
Abstract not provided.
SasquatchDeepCloud Image Classification Training Application
Abstract not provided.
14 Results