Tier 1 — Papers Saining calls great
代表作 · 16Saining's spontaneous enumeration of the deep-learning canon — the "~20–25 papers that deeply shaped deep learning and AI." Saining keeps his own work out: asked "DiT 不算吗?" he answers "算 0.25" — only a quarter.
🔍 Decoding the garbled names
"Tension Solution Unit" → an attention paper · high confidence on "attention"
This transcript reliably renders self-attention as software attention / Software Tension (2:58:30, 2:58:45) — so "Tension" = attention. Since Transformer is listed separately, the best fit is the original attention paper, Bahdanau et al. 2014. ⚠️ Confirm by ear at ~2:22:04 — issues & PRs welcome.
Tier 2 — Saining's own work
10Covered in depth across the interview — but he keeps these out of the "great" tier (he rates DiT "0.25"). MoCo & MAE are Kaiming He–led, with Saining as a co-author.
Tier 3 — Other works & systems referenced
RL · world models · systemsMentioned in passing or as influences — the RL & world-model lineage, contemporary systems, and concepts.
✨ How this was built
Compiled from the full verbatim transcript — 4,926 timestamped segments — not secondhand summaries. That precision matters: popular recaps listed ConvNeXt and V-JEPA as mentioned, but those names never appear in the actual audio (0 hits). They were imported from Saining's CV.
Deep-linked timestamps
Every ▶ pill jumps straight into the Bilibili video at that exact second.
Three clear tiers
Separates "papers Saining calls great" from "Saining's own work" from "merely referenced."
Garbled-name forensics
ASR mistakes decoded with evidence and the precise second to verify by ear.
Full-transcript coverage
All 4,926 segments scanned and read in context, not skimmed.