Publications proposing novel machine learning methods are often primarily
rated by exhibited predictive performance on selected problems. In this
position paper we argue that predictive performance alone is not a good
indicator for the worth of a publication. Using it as such even fosters
problems like inefficiencies of the machine learning research community as a
whole and setting wrong incentives for researchers. We therefore put out a call
for the publication of "negative" results, which can help alleviate some of
these problems and improve the scientific output of the machine learning
research community. To substantiate our position, we present the advantages of
publishing negative results and provide concrete measures for the community to
move towards a paradigm where their publication is normalized.

机器学习方法、预测性能、负面结果、效率低下、科学产出

位置：机器学习中接受负面结果

Position: Embracing Negative Results in Machine Learning

End-to-end learning refers to training a possibly complex learning system by
applying gradient-based learning to the system as a whole. End-to-end learning
system is specifically designed so that all modules are differentiable. In
effect, not only a central learning machine, but also all "peripheral" modules
like representation learning and memory formation are covered by a holistic
learning process. The power of end-to-end learning has been demonstrated on
many tasks, like playing a whole array of Atari video games with a single
architecture. While pushing for solutions to more challenging tasks, network
architectures keep growing more and more complex.
In this paper we ask the question whether and to what extent end-to-end
learning is a future-proof technique in the sense of scaling to complex and
diverse data processing architectures. We point out potential inefficiencies,
and we argue in particular that end-to-end learning does not make optimal use
of the modular design of present neural networks. Our surprisingly simple
experiments demonstrate these inefficiencies, up to the complete breakdown of
learning.

本文讨论了端到端学习技术在复杂数据处理体系结构中的可扩展性，并指出了它存在的潜在缺陷，特别是在利用神经网络现有的模块化设计方面的不足。作者的简单实验证明了这些效率问题，甚至导致学习完全崩溃。