raminmh/CfC

Health v2 · maintenance only

Adoption not part of the score

1053 stars · 164 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Reference implementations of Closed-form Continuous-time (CfC) neural networks, a fast closed-form approximation of liquid time-constant networks for sequential and irregularly-sampled data. Includes TensorFlow and PyTorch modules plus training scripts for Physionet, IMDB, XOR, and Walker2d benchmarks.

Use cases

• model irregularly-sampled time series like clinical ICU data
• train continuous-time recurrent networks for sequence classification
• benchmark CfC against LSTM and ODE-based neural networks
• run reinforcement learning experiments with liquid neural networks
• reproduce results from the Nature Machine Intelligence CfC paper

When to choose

• you need efficient continuous-time sequence models without ODE solvers
• your data has irregular time sampling
• you want liquid neural network behavior with fast closed-form dynamics

When to avoid

• you need a maintained production library - use the ncps package instead
• you need standard feedforward or transformer architectures
• you work outside Python/TensorFlow/PyTorch ecosystems

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning artificial-intelligence python liquid-neural-networks neural-ode recurrent-neural-networks sequence-models continuous-time pytorch tensorflow research-code

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