MLCommons Algorithmic Efficiency is a benchmark and competition measuring neural network training speedups due to algorithmic improvements in both training algorithms and models.

Muon optimizer: +>30% sample efficiency with <3% wallclock overhead

🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.

Efficient optimizers

Reasoning Computers. Lambda Calculus, Fully Differentiable. Also Neural Stacks, Queues, Arrays, Lists, Trees, and Latches.