Neuromorphic Computing promises orders of magnitude improvement in energy
efficiency compared to traditional von Neumann computing paradigm. The goal is
to develop an adaptive, fault-tolerant, low-footprint, fast, low-energy
intelligent system by learning and emulating brain functionality which can be
realized through innovation in different abstraction layers including material,
device, circuit, architecture and algorithm. As the energy consumption in
complex vision tasks keep increasing exponentially due to larger data set and
resource-constrained edge devices become increasingly ubiquitous, spike-based
neuromorphic computing approaches can be viable alternative to deep
convolutional neural network that is dominating the vision field today. In this
book chapter, we introduce neuromorphic computing, outline a few representative
examples from different layers of the design stack (devices, circuits and
algorithms) and conclude with a few exciting applications and future research
directions that seem promising for computer vision in the near future.

神经形态计算有望比传统的冯・诺依曼计算模式在能效上提升数个数量级。其目标是通过学习和模拟大脑功能来开发自适应、容错、低占用、快速、低能耗的智能系统，可以通过在材料、器件、电路、架构和算法等不同抽象层面上进行创新来实现。随着复杂视觉任务的能耗因大型数据集而呈指数增长，并且资源受限的边缘设备变得越来越普遍，基于脉冲的神经形态计算方法可以成为在当前主导视觉领域的深度卷积神经网络的可行替代方案。在本书章节中，我们介绍了神经形态计算，概述了设计堆栈的几个代表性示例（器件、电路和算法），并总结了一些令人兴奋的应用和未来的研究方向，这些方向在近期的计算机视觉中似乎很有前景。

面向下一代计算机视觉的基于脉冲的神经形态计算

Spike-based Neuromorphic Computing for Next-Generation Computer Vision

We consider two problems of NMT domain adaptation using meta-learning. First,
we want to reach domain robustness, i.e., we want to reach high quality on both
domains seen in the training data and unseen domains. Second, we want our
systems to be adaptive, i.e., making it possible to finetune systems with just
hundreds of in-domain parallel sentences. We study the domain adaptability of
meta-learning when improving the domain robustness of the model. In this paper,
we propose a novel approach, RMLNMT (Robust Meta-Learning Framework for Neural
Machine Translation Domain Adaptation), which improves the robustness of
existing meta-learning models. More specifically, we show how to use a domain
classifier in curriculum learning and we integrate the word-level domain mixing
model into the meta-learning framework with a balanced sampling strategy.
Experiments on English$\rightarrow$German and English$\rightarrow$Chinese
translation show that RMLNMT improves in terms of both domain robustness and
domain adaptability in seen and unseen domains. Our source code is available at
this https URL

本文探讨了基于元学习的神经机器翻译领域自适应的两个问题；一是如何实现跨领域鲁棒性，即在训练数据包括两个领域时获得高质量的翻译结果；二是如何实现适应性，即仅利用数百个领域内的平行句子对系统进行微调。我们的研究表明，我们提出的元学习模型（RMLNMT）能够显著提高模型的领域鲁棒性和适应性，同时包括已知领域和未知领域。

机器翻译中提高领域鲁棒性和领域适应性

Improving Both Domain Robustness and Domain Adaptability in Machine  Translation

Bin Packing problems have been widely studied because of their broad
applications in different domains. Known as a set of NP-hard problems, they
have different vari- ations and many heuristics have been proposed for
obtaining approximate solutions. Specifically, for the 1D variable sized bin
packing problem, the two key sets of optimization heuristics are the bin
assignment and the bin allocation. Usually the performance of a single static
optimization heuristic can not beat that of a dynamic one which is tailored for
each bin packing instance. Building such an adaptive system requires modeling
the relationship between bin features and packing perform profiles. The primary
drawbacks of traditional AI machine learnings for this task are the natural
limitations of feature engineering, such as the curse of dimensionality and
feature selection quality. We introduce a deep learning approach to overcome
the drawbacks by applying a large training data set, auto feature selection and
fast, accurate labeling. We show in this paper how to build such a system by
both theoretical formulation and engineering practices. Our prediction system
achieves up to 89% training accuracy and 72% validation accuracy to select the
best heuristic that can generate a better quality bin packing solution.

利用深度学习方法构建自适应系统，通过大量训练数据、自动特征选择和快速准确的标记，选择最佳的启发式算法以生成高质量的装箱方案，从而克服了传统人工智能机器学习方法的限制。