The phenomenon of Big Data continues to present moving targets for the scientific and technological state-of-the-art. This work demonstrates that the solution space of these challenges has expanded with deep learning now moving beyond traditional applications in computer vision and natural language processing to diverse and core machine learning tasks such as learning with streaming and non-iid-data, partial supervision, and large volumes of distributed data while preserving privacy. We present a framework coalescing multiple deep methods and corresponding models as responses to specific Big Data challenges. First, we perform a detailed per-challenge review of existing techniques, with benchmarks and usage advice, and subsequently synthesize them together into one organic construct that we discover principally uses extensions of one underlying model, the autoencoder. This work therefore provides a synthesis where challenges at scale across the Vs of Big Data could be addressed by new algorithms and architectures being proposed in the deep learning community. The value being proposed to the reader from either community in terms of nomenclature, concepts, and techniques of the other would advance the cause of multi-disciplinary, transversal research and accelerate the advance of technology in both domains.

中文翻译：

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应对持续和新出现的大数据挑战的潜在深度学习解决方案——从业者的食谱

大数据现象继续为最先进的科学技术提供移动目标。这项工作表明，随着深度学习的发展，这些挑战的解决空间已经扩大，现在已经从计算机视觉和自然语言处理中的传统应用转向多样化和核心的机器学习任务，例如使用流式和非 iid 数据进行学习、部分监督和大量分布式数据，同时保护隐私。我们提出了一个结合多种深度方法和相应模型的框架，以应对特定的大数据挑战。首先，我们对现有技术进行详细的每次挑战审查，并提供基准和使用建议，然后将它们合成到一个有机结构中，我们发现该结构主要使用一个基础模型的扩展，即自动编码器。因此，这项工作提供了一种综合，其中可以通过深度学习社区中提出的新算法和架构来解决大数据 Vs 中的大规模挑战。任何一个社区在命名、概念和技术方面向读者提出的价值将推动多学科、横向研究的事业，并加速两个领域的技术进步。