71 citations · 127 across the 14 of their papers we have counts for
14 papers
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
Hector Zenil, Jesper Tegnér, Felipe S. Abrahão +17
Recent advances in machine learning and AI, including Generative AI and LLMs, are disrupting technological innovation, product development, and society as a whole. AI's contributio…
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Weitao Du, Yuanqi Du, Limei Wang +5
Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects. Despite rapid progress in encoding 3D symmetries into Graph Neural Networks (GNNs), a co…
Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction
Junwen Bai, Yuanqi Du, Yingheng Wang +3
Modern machine learning techniques have been extensively applied to materials science, especially for property prediction tasks. A majority of these methods address scalar property…
Monitoring Vegetation From Space at Extremely Fine Resolutions via Coarsely-Supervised Smooth U-Net
Joshua Fan, Di Chen, Jiaming Wen +2
Monitoring vegetation productivity at extremely fine resolutions is valuable for real-world agricultural applications, such as detecting crop stress and providing early warning of…
Left Heavy Tails and the Effectiveness of the Policy and Value Networks in DNN-based best-first search for Sokoban Planning
Dieqiao Feng, Carla Gomes, Bart Selman
Despite the success of practical solvers in various NP-complete domains such as SAT and CSP as well as using deep reinforcement learning to tackle two-player games such as Go, cert…
Constrained Machine Learning: The Bagel Framework
Guillaume Perez, Sebastian Ament, Carla Gomes +1
Machine learning models are widely used for real-world applications, such as document analysis and vision. Constrained machine learning problems are problems where learned models h…