The learning rate is one of the most important hyper-parameters for model
training and generalization. However, current hand-designed parametric learning
rate schedules offer limited flexibility and the predefined schedule may not
match the training dynamics of high dimensional and non-convex optimization
problems. In this paper, we propose a reinforcement learning based framework
that can automatically learn an adaptive learning rate schedule by leveraging
the information from past training histories. The learning rate dynamically
changes based on the current training dynamics. To validate this framework, we
conduct experiments with different neural network architectures on the Fashion
MINIST and CIFAR10 datasets. Experimental results show that the auto-learned
learning rate controller can achieve better test results. In addition, the
trained controller network is generalizable -- able to be trained on one data
set and transferred to new problems.

本文提出了基于强化学习的框架，通过利用过去训练历史的信息来自动学习自适应学习率调度器，验证实验结果表明，自动学习学习率控制器能够获得更好的测试结果，并且经过训练的控制器网络是可迁移的。

学习自适应学习率调度

Learning an Adaptive Learning Rate Schedule

Simultaneous machine translation begins to translate each source sentence
before the source speaker is finished speaking, with applications to live and
streaming scenarios. Simultaneous systems must carefully schedule their reading
of the source sentence to balance quality against latency. We present the first
simultaneous translation system to learn an adaptive schedule jointly with a
neural machine translation (NMT) model that attends over all source tokens read
thus far. We do so by introducing Monotonic Infinite Lookback (MILk) attention,
which maintains both a hard, monotonic attention head to schedule the reading
of the source sentence, and a soft attention head that extends from the
monotonic head back to the beginning of the source. We show that MILk's
adaptive schedule allows it to arrive at latency-quality trade-offs that are
favorable to those of a recently proposed wait-k strategy for many latency
values.

我们使用 Monotonic Infinite Lookback（MILk）注意力机制来同时翻译，并学习一种自适应调度模式，以平衡质量和延迟，并展示其与最近提出的延迟策略相比，更有利于延迟 - 质量权衡。