Bringing Criticism to Learning Deep Learning

Disclaimer: This article is written while the author is drunk. Expect errors and grammar mistakes.

There's been something lurking in my mind for years–something that can be described as a dream, or more precisely, as an objective. It is resurrected every time I recalled the biology seminars I took. I can remember all the fierce firefight with professors and fellow students over my slides and presentations. Every seminar session is a battlefield with people desperately searching for logical errors, data inconsistencies, misleading figures, and even grammar mistakes, to falsify the presenter's work.

After I entered the field of computer science and engineering, things become much different. In nearly every single seminar I attended, I can only see peace and friendly discussion. There's been no one asking tough questions. In a biologist's eyes, tons of deep learning papers lacks enough evidence to prove their conclusion. Yet they got published into top conferences and journals. It seems that people nowadays only cares about performance.

Learning deep learning concretely is merely a distant dream.