"The paper that killed deep learning theory" by LawrenceC

Around 10 years ago, a paper came out that arguably killed classical deep learning theory: Zhang et al. 's aptly titled Understanding deep learning requires rethinking generalization. Of course, this is a bit of an exaggeration. No single paper ever kills a field of research on its own, and deep learning theory was not exactly the most productive and healthy field at the time this was published. But if I had to point to a single paper that shattered the feeling of optimism at the time, it would be Zhang et al. 2016.[1] Caption: believe it or not, this unassuming table rocked the field of deep learning theory back in 2016, despite probably involving fewer computational resources than what Claude 4.7 Opus consumed when I clicked the “Claude” button embedded into the LessWrong editor. — Let's start by answering a question: what, exactly, do I mean by deep learning theory? At least in 2016, the answer was: “extending statistical learning theory to deep neural networks trained with SGD, in order to derive generalization bounds that would explain their behavior in practice”. — Since its conception in the mid 1980s, statistical learning theory had been the dominant approach for [...] The original text contained 2 footnotes which were omitted from this narration. --- First published: April 25th, 2026 Source: https://www.lesswrong.com/posts/ZvQfcLbcNHYqmvWyo/the-paper-that-killed-deep-learning-theory --- Narrated by TYPE III AUDIO. ---Images from the article: