CfC

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CfC is an open-source AI tool that provides Closed-form Continuous-time Neural Networks. It offers powerful sequential liquid neural information processing units for AI researchers and developers.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is CfC?

CfC (Closed-form Continuous-time Neural Networks) is an open-source library providing implementations of powerful sequential liquid neural information processing units. It offers various versions of CfC models in both TensorFlow 2.x and PyTorch. The library includes training scripts for different datasets such as Physionet 2012, XOR, IMDB, and Walker2d, adapting code from established research. CfC is designed for AI researchers and developers working with continuous-time models and liquid neural networks, offering flexibility with flags to run minimal versions, use LTC, or mix with LSTM for gradient handling.

Best used for

Ideal for developers and data scientists who need to implement and experiment with advanced continuous-time neural networks, train models on diverse datasets, and explore liquid neural network architectures. Especially valuable for AI researchers looking for open-source implementations in TensorFlow and PyTorch.

Common actions

implement neural networks

train AI models

research continuous-time models

develop liquid neural networks

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Capabilities

Key features

TensorFlow 2.x implementation
PyTorch implementation
Training scripts included
Liquid neural networks
Continuous-time models
Flexible model configurations

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What programming languages and frameworks does CfC support?

CfC provides implementations in both TensorFlow 2.x and PyTorch, making it accessible to developers familiar with either of these popular deep learning frameworks. It also requires Python 3.6 or newer, pytorch-lightning, and scikit-learn.

Can CfC models be trained on different types of datasets?

Yes, CfC includes training scripts for various datasets, demonstrating its versatility. Examples include Physionet 2012 for medical time-series, XOR for basic logic, IMDB for sentiment analysis, and Walker2d for reinforcement learning environments.

Are there different configurations available for CfC models?

CfC offers flexible configurations through command-line flags. Users can choose to run the model without a gate, use a minimal direct solution, integrate with an LTC (Liquid Time-Constant) model, or mix the CfC's RNN-state with an LSTM to mitigate vanishing gradients.

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