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Publications about AI methods and applications.
Research paper by Hongyu Qu and collaborators. arXiv record dated 2025-09-18. Research area: Machine learning.
Research paper by Daosheng Xu and collaborators. arXiv record dated 2025-02-22. Research area: Machine learning.
Research paper by Asanobu Kitamoto and collaborators. arXiv record dated 2026-10-15. Research area: Computer vision.
Research paper by Xuefei Wang and collaborators. arXiv record dated 2026-08-31. Research area: Machine learning.
Advances in remote sensing technologies have made it possible to use high-resolution visual data for weather observation and forecasting tasks. We propose the use of multi-layer neural networks for understanding complex atmospheric dynamics based on multichannel satellite images. The capability of our model was evaluated by using a linear regression task for single typhoon coordinates prediction. A specific combination of models and different activation policies enabled us to obtain an interesting prediction result in the northeastern hemisphere (ENH).
Research paper by Lincan Li and collaborators. arXiv record dated 2025-11-22. Research area: Language technology.
This paper introduces Typhoon 2, a series of text and multimodal large language models optimized for the Thai language. The series includes models for text, vision, and audio. Typhoon2-Text builds on state-of-the-art open models, such as Llama 3 and Qwen2, and we perform continual pre-training on a mixture of English and Thai data. We employ post-training techniques to enhance Thai language performance while preserving the base models' original capabilities. We release text models across a range of sizes, from 1 to 70 billion parameters, available in both base and instruction-tuned variants. To guardrail text generation, we release Typhoon2-S
Typhoon is a series of Thai large language models (LLMs) developed specifically for the Thai language. This technical report presents challenges and insights in developing Thai LLMs, including data preparation, pretraining, instruction-tuning, and evaluation. As one of the challenges of low-resource languages is the amount of pretraining data, we apply continual training to transfer existing world knowledge from a strong LLM. To evaluate the Thai knowledge encapsulated in each model from the pretraining stage, we develop ThaiExam, a benchmark based on examinations for high-school students and investment professionals in Thailand. In addition,
Research paper by Ahmet Caner Yüzügüler and collaborators. arXiv record dated 2026-02-12. Research area: Machine learning.
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