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Found 6 papers, 0 papers with code
AviationGPT: A Large Language Model for the Aviation Domain
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.
Adapting Sentence Transformers for the Aviation Domain
Learning effective sentence representations is crucial for many Natural Language Processing (NLP) tasks, including semantic search, semantic textual similarity (STS), and clustering.
Remote Sensing Scene Classification with Masked Image Modeling (MIM)
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.
Aerial Image Object Detection With Vision Transformer Detector (ViTDet)
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.
Airport Taxi Time Prediction and Alerting: A Convolutional Neural Network Approach
This paper proposes a novel approach to predict and determine whether the average taxi- out time at an airport will exceed a pre-defined threshold within the next hour of operations.
Multi-Airport Delay Prediction with Transformers
Traffic, demand, weather, and traffic management actions are all critical inputs to any prediction model.
