Public learning path · 3 papers
Reliable paper reading
A three-paper practice path for tracing claims through architecture, experiments, and empirical scaling evidence.
- For
- Readers who want a repeatable, evidence-linked workflow for technical papers.
- Curator
- DeepStudy editorial
For each paper, identify the central claim, locate the experiment or equation supporting it, and record what the evidence does not establish.
Reading order
Required · Guide ready
Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin · 2017
Practice mapping architecture claims to equations and reported translation results.
Required · Guide ready
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun · 2015
Compare the proposed residual formulation with the reported optimization evidence.
Required · Guide ready
Scaling Laws for Neural Language Models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei · 2020
Separate fitted empirical relationships from causal or out-of-distribution claims.