Jun 2, 2021 · Abstract:A core issue with learning to optimize neural networks has been the lack of generalization to real world problems.
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Jun 7, 2021 · A core issue with learning to optimize neural networks has been the lack of generalization to real world problems.
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Jun 5, 2021 · So, in practice, this method finds optimal optimizer hyperparameters sequentially based on input statistics. Like in many NAS(Neural ...
Sep 15, 2024 · This system outperforms Adam at all neural network tasks including on modalities not seen during training. We achieve 2x speedups on ImageNet, ...
Jun 2, 2021 · This work describes a system designed from a generalization-first perspective, learning to update optimizer hyperparameters instead of model ...
In this paper, we propose several flatness-aware regularizers to improve both optimizer and op- timizee generalization abilities of current state-of-the-art L2O ...
A promising data-driven approach, learning to optimize. (L2O), arises from the meta-learning community to alle- viate this issue (Chen et al., 2022). It aims to ...
LHOPT. Code for inner optimizer (CIAO) and an example training config for "A Generalizable Approach to Learning Optimizers".
Abstract. Learning to optimize (L2O) has gained increasing popularity, which automates the design of optimizers by data-driven approaches.
Nov 1, 2024 · In this paper, we propose a learning based model called Symbolic Optimizer Learner (SOL) that can discover high-performance symbolic optimizers ...