关于深度学习中科学与工程结合的研讨会后报告，NeurIPS 2019，温哥华
Post-Workshop Report on Science meets Engineering in Deep Learning, NeurIPS 2019, Vancouver
摘要：
深度学习中的科学与工程结合在温哥华举行，作为NeurIPS 2019研讨会的一部分。作为研讨会的组织者，我们创建了以下报告，试图将整个活动中出现的新主题和反复出现的主题分离出来。深度学习尽管近年来取得了巨大的成功，但它仍然是艺术和工程的复杂组合。本次研讨会旨在将所有人聚集在一起，共同解决他们正在处理的问题中看似截然不同的挑战。作为研讨会的一部分，我们特别关注架构、数据和优化之间的相互依赖性，这些相互依赖性导致了设计和性能复杂性的巨大景观，而这些景观尚未得到很好的理解。今年，我们的目标是在我们的社区中强调以下方向：（i）确定通往更好的模型和算法的障碍;（ii）确定我们希望建立科学和潜在理论理解的一般趋势;（iii）确定我们希望建立的科学和潜在理论理解的一般趋势。以及（iii）科学实验和实验方案的严格设计，其目的是解决和查明谜团的起源，同时确保可重复性结论的稳健性。结果，这些主题出现并得到了广泛讨论，符合我们的期望，并为这些方向的新研究铺平了道路。虽然我们承认，文本通过我们的镜头自然是有偏见的，但我们在这里试图公平地突出研讨会的成果。
英文摘要：
Science meets Engineering in Deep Learning took place in Vancouver as part of the Workshop section of NeurIPS 2019. As organizers of the workshop, we created the following report in an attempt to isolate emerging topics and recurring themes that have been presented throughout the event. Deep learning can still be a complex mix of art and engineering despite its tremendous success in recent years. The workshop aimed at gathering people across the board to address seemingly contrasting challenges in the problems they are working on. As part of the call for the workshop, particular attention has been given to the interdependence of architecture, data, and optimization that gives rise to an enormous landscape of design and performance intricacies that are not well-understood. This year, our goal was to emphasize the following directions in our community: (i) identify obstacles in the way to better models and algorithms; (ii) identify the general trends from which we would like to build scientific and potentially theoretical understanding; and (iii) the rigorous design of scientific experiments and experimental protocols whose purpose is to resolve and pinpoint the origin of mysteries while ensuring reproducibility and robustness of conclusions. In the event, these topics emerged and were broadly discussed, matching our expectations and paving the way for new studies in these directions. While we acknowledge that the text is naturally biased as it comes through our lens, here we present an attempt to do a fair job of highlighting the outcome of the workshop.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
