e-means beats k-means in 1.4 epochs, delivering 10-45x GPU speedups on embeddings
pmttyji · reddit · 2026-11-12
Researchers open-sourced e-means, a mini-batch k-means variant using an exponential moving average optimization built on modern deep learning machinery, now in faiss's contrib directory.
- Reaches the loss of Lloyd's k-means at 25 epochs in just 1.4–2.6 epochs across embedding datasets — 10–45x GPU wall-clock speedups over state-of-the-art implementations
- Establishes scaling laws tying decay rate and batch size to final performance, so hyperparameters can be fixed across problem sizes without sweeps
- Scales to DINO10B: partitions 10 billion DINO embeddings into 16 million clusters in 2.5 epochs
- Paper, code, and a browser playground are all available
Related event: Meta FAIR Open-Sources e-means, a k-means Algorithm 10-45x Faster(2 posts)→
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