A Look Back at DeepMind's Grid-LSTM
prajdabre · x · 2026-07-19
The author revisits the era of diverse neural network architectures that preceded Transformer, using Grid-LSTM (2015, DeepMind) as a primary example.
They explain that the core of Grid-LSTM was that a cell no longer had a single output and memory state, but multiple states, allowing the same set of inputs to be observed and interact along multiple axes. The author likens this approach to a "multi-perspective data representation."
They further connect this to later concepts like multi-head attention and "parameter sharing across time and depth axes," suggesting these ideas look like precursors to today's universal/looping transformer. The final verdict: this work seemed outrageous at the time, but in hindsight, it was incredibly ahead of its time.
More from Research
- Stanford Team Introduces Gigatoken, the World's Fastest Tokenizer — StanfordAILab · 2026-07-22
- Tabul AI launches Metal TreeSHAP to speed up Shapley values on Apple silicon — Scobleizer · 2026-07-22
- Reddit points to OpenAI’s ChatGPT Ads page — EcstaticAsparagus509 · 2026-07-22
- Open-source runtime lets each repo define its own AI code reviewer — ibabufrik · 2026-07-22
- DeepSWE: A New Benchmark for Evaluating AI Coding Agents on Real GitHub Issues — pmz · 2026-07-22
- A Rust space-economy sim runs hundreds of autonomous ships, built with Claude — kalcode · 2026-07-22