no code implementations • 29 Nov 2023 • Liya Wang, Jason Chou, Xin Zhou, Alex Tien, Diane M Baumgartner
The advent of ChatGPT and GPT-4 has captivated the world with large language models (LLMs), demonstrating exceptional performance in question-answering, summarization, and content generation.
no code implementations • 16 May 2023 • Liya Wang, Jason Chou, Dave Rouck, Alex Tien, Diane M Baumgartner
Learning effective sentence representations is crucial for many Natural Language Processing (NLP) tasks, including semantic search, semantic textual similarity (STS), and clustering.
no code implementations • 28 Feb 2023 • Liya Wang, Alex Tien
As Masked Image Modeling (MIM), a self-supervised learning (SSL) technique, has been shown as a better way for learning visual feature representation, it presents a new opportunity for improving ML performance on the scene classification task.
no code implementations • 28 Jan 2023 • Liya Wang, Alex Tien
Our results show that ViTDet can consistently outperform its convolutional neural network counterparts on horizontal bounding box (HBB) object detection by a large margin (up to 17% on average precision) and that it achieves the competitive performance for oriented bounding box (OBB) object detection.
no code implementations • 4 Nov 2021 • Liya Wang, Amy Mykityshyn, Craig Johnson, Jillian Cheng
This work was conducted in support of a MITRE-developed mobile application, Pacer, which displays predicted departure demand to general aviation (GA) flight operators so they can have better situation awareness of the potential for departure delays during busy periods.
no code implementations • 4 Nov 2021 • Liya Wang, Alex Tien, Jason Chou
Traffic, demand, weather, and traffic management actions are all critical inputs to any prediction model.
no code implementations • 3 Mar 2021 • Liya Wang, Panta Lucic, Keith Campbell, Craig Wanke
Our approach can also identify mislabeled aircraft types in the flight track data and find true types for records with pseudo aircraft type labels such as HELO.
no code implementations • 6 Nov 2020 • Liya Wang, Amy Mykityshyn, Craig Johnson, Benjamin D. Marple
Field demonstrations involving Pacer's previously designed rule-based prediction method showed that the prediction accuracy of departure demand still has room for improvement.
no code implementations • 3 Nov 2020 • Liya Wang, Panta Lucic, Keith Campbell, Craig Wanke
The current practice of manually processing features for high-dimensional and heterogeneous aviation data is labor-intensive, does not scale well to new problems, and is prone to information loss, affecting the effectiveness and maintainability of machine learning (ML) procedures.