Ha, David

Learning Latent Dynamics for Planning from Pixels

Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., & Davidson, J. (2018) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 708

Weight Agnostic Neural Networks

Gaier, A., & Ha, D. (2019) · Unpublished

Neural Networks and ApplicationsAdvanced Neural Network Applications

Cited by 161

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Lu, C., Lu, C., Lange, R. T., Foerster, J., Clune, J., & Ha, D. (2024) · arXiv

Artificial Intelligence ApplicationsTopic Modeling

Cited by 118

Towards end-to-end automation of AI research

Lu, C., Lu, C., Lange, R. T., Yamada, Y., Hu, S., Foerster, J., Ha, D., & Clune, J. (2026) · Nature

Artificial Intelligence ApplicationsScientific Computing and Data Management

Cited by 116

Collective intelligence for deep learning: A survey of recent developments

Ha, D., & Tang, Y. (2022) · Collective Intelligence

Neural Networks and ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 71

Evolutionary optimization of model merging recipes

Akiba, T., Shing, M., Tang, Y., Sun, Q., & Ha, D. (2025) · Nature Machine Intelligence

Topic ModelingNatural Language Processing Techniques

Cited by 58

The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

Yamada, Y., Lange, R. T., Lu, C., Hu, S., Lu, C., Foerster, J., Clune, J., & Ha, D. (2025) · arXiv

Artificial Intelligence ApplicationsAcademic Writing and Publishing

Cited by 32