Significant efforts have been made to make the training more efficient such as momentum, learning rate scheduling, weight regularization, and meta-learning.
Based on those results, it is clear that even the most basic curve fitting can forecast future weights, resulting in reducing training time. 4. Page 5. Learning ...
Authors: Jinhyeok Jang, Woo-han Yun, Won Hwa Kim, Youngwoo Yoon, Jaehong Kim, Jaeyeon Lee, ByungOk Han. Issue Date: 2023-07.
As an add-on module, WNN predicts the future weights to make the learning process faster regardless of tasks and architectures. Experimental results show that ...
0 About · Learning to Boost Training by Periodic Nowcasting Near Future Weights. 04:55. Learning to Boost Training by Periodic Nowcasting Near Future Weights.
Jul 3, 2023 · Our paper "Learning to Boost Training by Periodic Nowcasting Near Future Weights" will be presented in ICML 2023. The proposed Weight Nowcasting ...
... Learning to Boost Training by Periodic Nowcasting Near Future Weights," ICML 2023 [Github]. Antoine Maiorca, Youngwoo Yoon, and Thierry Dutoit, "Validating ...
Sep 6, 2024 · Neural network training can be accelerated when a learnable update rule is used in lieu of classic adaptive optimizers (e.g. Adam).
Learning to Boost Training by Periodic Nowcasting Near Future Weights ... is a long-term project to advance science through improved peer ...
I am interested in problems at the intersection of HCI, robotics, and machine learning. ... Learning to Boost Training by Periodic Nowcasting Near Future Weights