Prehistory — From Neural Networks to the TransformerKaiming He et al. (Microsoft Research Asia)Microsoft Research

He et al. publish Deep Residual Learning for Image Recognition (ResNet) on arXiv

Deep Residual Learning for Image Recognition (ResNet) released on arXiv

Why it matters

Made training of very deep networks possible through residual connections. A basic component of most later architectures, including the Transformer

Source — Primary sources first; secondary reports are labelled “Press”

Paperarxiv.orghttps://arxiv.org/abs/1512.03385

Added in Week 35, 2026 · See that week

Same week

Nonprofit research lab OpenAI is announced, with $1 billion pledgedOfficial

Nearby on the same axis

Nonprofit research lab OpenAI is announced, with $1 billion pledgedOfficialGoogle open-sources TensorFlow under Apache 2.0OfficialMastering the game of Go with deep neural networks and tree searchPaperAlphaGo–Lee Sedol five-game match in Seoul ends (Mar 9–15)Official