5 citations · 12 across the 6 of their papers we have counts for
6 papers
AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators
Jason Chou, Ao Liu, Yuchi Deng +13
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, with code generation emerging as a key area of focus. While numerous benchmarks have…
AviationGPT: A Large Language Model for the Aviation Domain
Liya Wang, Jason Chou, Xin Zhou +2
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…
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…
Generated Loss and Augmented Training of MNIST VAE
Jason Chou
The variational autoencoder (VAE) framework is a popular option for training unsupervised generative models, featuring ease of training and latent representation of data. The objec…
Generated Loss, Augmented Training, and Multiscale VAE
Jason Chou, Gautam Hathi
The variational autoencoder (VAE) framework remains a popular option for training unsupervised generative models, especially for discrete data where generative adversarial networks…