Search papers, labs, and topics across Lattice.
5
0
7
6
This study implements a hybrid-structure-based deep learning framework to generate monthly 2 m specific humidity, 2 m temperature and surface pressure at 1/30° × 1/30° horizontal resolution during 1901–2023, and reproduces learned terrain-related spatial gradients without explicitly enforcing physical equations or terrain constraints.
Achieving up to 338x faster inference, EOVSAM redefines open-vocabulary segmentation without sacrificing accuracy.
Token-oriented inference optimizations can cut production costs and boost efficiency, transforming large model services from merely callable to fully operable.
HiMPO assigns less-entangled credit to memory updates, significantly boosting long-horizon agent performance while minimizing blame leakage from errors.
Diffusion models can be made more efficient and produce better outputs by dynamically allocating compute based on a learned "difficulty" signature, without any retraining.
