Blealtan/efficient-kan

Health v2 · maintenance only

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Adoption not part of the score

4655 stars · 414 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

An efficient pure-PyTorch implementation of Kolmogorov-Arnold Networks (KAN) that reformulates the computation as matrix multiplications over B-spline basis functions. It significantly reduces memory cost compared to the original implementation while supporting forward and backward passes.

Use cases

• train Kolmogorov-Arnold Networks efficiently in PyTorch
• replace MLP layers with KAN layers in experiments
• run KAN models on MNIST-style classification tasks
• experiment with spline-based learnable activation functions
• reduce memory usage when training KAN models

When to choose

• you want a fast, memory-efficient KAN implementation in pure PyTorch
• you need KAN layers that integrate with standard PyTorch training loops
• the original pykan implementation is too slow or memory-hungry for your workload

When to avoid

• you need the exact L1-on-activations regularization from the original KAN paper
• you require the full feature set and interpretability tooling of the original pykan
• you need a framework other than PyTorch

Facets

library · maturity active

machine-learning deep-learning machine-learning deep-learning python kolmogorov-arnold-network kan pytorch neural-networks b-splines

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markdown · JSON · MCP: product_card(name="Blealtan/efficient-kan")