2 citations · 2 across the 6 of their papers we have counts for
6 papers
Adapting Sentence Transformers for the Aviation Domain
Liya Wang, Jason Chou, Dave Rouck +2
Learning effective sentence representations is crucial for many Natural Language Processing (NLP) tasks, including semantic search, semantic textual similarity (STS), and clusterin…
Multi-Airport Delay Prediction with Transformers
Liya Wang, Alex Tien, Jason Chou
Airport performance prediction with a reasonable look-ahead time is a challenging task and has been attempted by various prior research. Traffic, demand, weather, and traffic manag…
Flight Demand Forecasting with Transformers
Liya Wang, Amy Mykityshyn, Craig Johnson +1
Transformers have become the de-facto standard in the natural language processing (NLP) field. They have also gained momentum in computer vision and other domains. Transformers can…
Helicopter Track Identification with Autoencoder
Liya Wang, Panta Lucic, Keith Campbell +1
Computing power, big data, and advancement of algorithms have led to a renewed interest in artificial intelligence (AI), especially in deep learning (DL). The success of DL largely…
Autoencoding Features for Aviation Machine Learning Problems
Liya Wang, Panta Lucic, Keith Campbell +1
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 i…
Deep Learning for Flight Demand Forecasting
Liya Wang, Amy Mykityshyn, Craig Johnson +1
Inspired by the success of deep learning (DL) in natural language processing (NLP), we applied cutting-edge DL techniques to predict flight departure demand in a strategic time hor…