no code implementations • NeurIPS GLFrontiers Workshop 2023 • Dolores Garcia, Gregor Kržmanc, Philipp Zehetner, Jan Kieseler, Michele Selvaggi
Reconstructing particles properties from raw signals measured in particle physics detectors is a challenging task due to the complex shapes of the showers, variety in density and sparsity.
no code implementations • NeurIPS 2023 • Qian Huang, Hongyu Ren, Peng Chen, Gregor Kržmanc, Daniel Zeng, Percy Liang, Jure Leskovec
In-context learning is the ability of a pretrained model to adapt to novel and diverse downstream tasks by conditioning on prompt examples, without optimizing any parameters.