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Learning Gradient Descent: Better Generalization and Longer Horizons
Kaifeng Lyu · Shunhua Jiang · Jian Li

Tue Aug 08 01:30 AM -- 05:00 AM (PDT) @ Gallery #16

Training deep neural networks is a highly nontrivial task, involving carefully selecting appropriate training algorithms, scheduling step sizes and tuning other hyperparameters. Trying different combinations can be quite labor-intensive and time consuming. Recently, researchers have tried to use deep learning algorithms to exploit the landscape of the loss function of the training problem of interest, and learn how to optimize over it in an automatic way. In this paper, we propose a new learning-to-learn model and some useful and practical tricks. Our optimizer outperforms generic, hand-crafted optimization algorithms and state-of-the-art learning-to-learn optimizers by DeepMind in many tasks. We demonstrate the effectiveness of our algorithms on a number of tasks, including deep MLPs, CNNs, and simple LSTMs.

Author Information

Kaifeng Lyu (Tsinghua University)
Shunhua Jiang (Tsinghua University)
Jian Li (IIIS)

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