Troubling Trends in Machine Learning Scholarship
A pertinent critique of recent trends within machine learning scholarships, the author makes the following salient criticism:
- Failure to distinguish between explanation and speculation.
- Failure to identify the sources of empirical gains, e.g. emphasizing unnecessary modifications to neural architectures when gains actually stem from hyper-parameter tuning.
- Mathiness: the use of mathematics that obfuscates or impresses rather than clarifies, e.g. by confusing technical and non-technical concepts.
- Misuse of language, e.g. by choosing terms of art with colloquial connotations or by overloading established technical terms.
http://approximatelycorrect.com/2018/07/10/troubling-trends-in-machine-learning-scholarship/

Using Deep Learning to Automatically Rank Millions of Hotel Images
An interesting post, looking at how idealo.de built a deep learning model to automatically assess image quality by implementing an aesthetic and technical image quality classifier based on Google’s research paper “NIMA: Neural Image Assessment”.
https://medium.com/idealo-tech-blog/using-deep-learning-to-automatically-rank-millions-of-hotel-images-c7e2d2e5cae2


Glow: Better Reversible Generative Models
blog.openai.com – Share
OpenAI introduces Glow, a reversible generative model which uses invertible 1x1 convolutions. The model can generate realistic high resolution images, supports efficient sampling, and discovers features that can be used to manipulate attributes of data. The code will be released and there is a fun online visualization tool.
https://blog.openai.com/glow/