Transformer as Linear Expansion of Learngene

1 code implementation • 9 Dec 2023 • Shiyu Xia, Miaosen Zhang, Xu Yang, Ruiming Chen, Haokun Chen, Xin Geng

Under the situation where we need to produce models of varying depths adapting for different resource constraints, TLEG achieves comparable results while reducing around 19x parameters stored to initialize these models and around 5x pre-training costs, in contrast to the pre-training and fine-tuning approach.

Machine Learning Methods for Track Classification in the AT-TPC

2 code implementations • 21 Oct 2018 • Michelle P. Kuchera, Raghuram Ramanujan, Jack Z. Taylor, Ryan R. Strauss, Daniel Bazin, Joshua Bradt, Ruiming Chen

We evaluate machine learning methods for event classification in the Active-Target Time Projection Chamber detector at the National Superconducting Cyclotron Laboratory (NSCL) at Michigan State University.

BIG-bench Machine Learning Classification +3