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Blealtan avatar

Blealtan/efficient-kan

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Efficient Kan

This project is a PyTorch library for building and training Kolmogorov-Arnold Networks. It implements a neural network architecture that replaces fixed activation functions with learnable spline-based functions on edges, serving as a tool for interpretable machine learning.

The implementation utilizes reformulated matrix operations to reduce memory overhead and increase computation speed. It employs L1 regularization to sparsify network weights, which improves the transparency of the model's internal logic and decisions.

The framework covers a range of capabilities including grid-based function approximation, B-spline activation functions, and deep learning model optimization. These features are built using native PyTorch tensors to support automatic differentiation and hardware acceleration.

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Features

  • Neural Network Architectures - Implements a novel neural network architecture that replaces fixed activations with learnable spline functions on edges.
  • B-Spline Activations - Implements learnable B-spline activation functions on network edges to allow flexible non-linear transformations.
  • Kolmogorov-Arnold Networks - Provides a high-performance implementation of Kolmogorov-Arnold Networks using reformulated matrix operations.
  • Model Interpretability Tools - Functions as a tool for interpretable machine learning by leveraging L1 regularization to clarify model decisions.
  • Neural Network Interpretability - Enhances model transparency by using weight sparsification to make internal network logic easier to understand.
  • PyTorch Implementations - Provides a complete PyTorch-native implementation of Kolmogorov-Arnold Networks for research and training.
  • Spline-Based Approximations - Uses spline-based approximations to map input values to a grid of control points for function approximation.
  • L1 Regularization - Applies L1 regularization to penalize absolute weight values and induce sparsity for better interpretability.
  • Deep Learning Optimization - Optimizes deep learning model execution by reducing memory overhead and increasing computation speed via reformulated matrix operations.
  • Model Sparsification - Includes utilities for model weight sparsification via L1 regularization to improve interpretability.
  • Matrix-Reformulated Operations - Reformulates network operations as standard matrix multiplications to leverage GPU acceleration and PyTorch tensors.
4,662 stars·415 forks·Python·MIT·25 views

Star history

Star history chart for blealtan/efficient-kanStar history chart for blealtan/efficient-kan

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does blealtan/efficient-kan do?

This project is a PyTorch library for building and training Kolmogorov-Arnold Networks. It implements a neural network architecture that replaces fixed activation functions with learnable spline-based functions on edges, serving as a tool for interpretable machine learning.

What are the main features of blealtan/efficient-kan?

The main features of blealtan/efficient-kan are: Neural Network Architectures, B-Spline Activations, Kolmogorov-Arnold Networks, Model Interpretability Tools, Neural Network Interpretability, PyTorch Implementations, Spline-Based Approximations, L1 Regularization.

Which projects share features with blealtan/efficient-kan?

Projects with overlapping indexed features include: kindxiaoming/pykan — pykan is a library for implementing Kolmogorov-Arnold Networks, replacing fixed node activation functions with… christophm/interpretable-ml-book — This project is a comprehensive educational resource and technical manual focused on interpretable machine learning… nvidia/model-optimizer — Model-Optimizer is a deep learning toolkit and framework dedicated to compressing, pruning, quantizing, and optimizing… cs231n/cs231n.github.io — This project is a static educational website and comprehensive curriculum focused on computer vision and deep… quark0/darts — Darts is a differentiable architecture search framework and library designed to automate the discovery of optimal… facebookresearch/slowfast — SlowFast is a PyTorch video understanding framework and spatiotemporal neural network library. It serves as a toolset…

Projects sharing features with Efficient Kan

These projects share indexed features with Efficient Kan. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • kindxiaoming/pykanKindXiaoming avatar

    KindXiaoming/pykan

    16,340View on GitHub↗

    pykan is a library for implementing Kolmogorov-Arnold Networks, replacing fixed node activation functions with learnable spline functions located on the network edges. It serves as an interpretable AI framework and symbolic regression tool designed to derive transparent mathematical rules from complex data. The project focuses on converting learned numerical functions into human-readable symbolic expressions through library matching and formula conversion. It utilizes additive-compositional topologies and learnable piecewise polynomial segments to approximate non-linear mappings. The framewo

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  • christophm/interpretable-ml-bookchristophM avatar

    christophM/interpretable-ml-book

    5,317View on GitHub↗

    This project is a comprehensive educational resource and technical manual focused on interpretable machine learning and explainable AI. It serves as a textbook and reference for implementing techniques that make complex machine learning models transparent and understandable to humans. The resource provides guidance on both building inherently transparent models, such as decision trees and sparse linear models, and applying post-hoc explanation methods to black-box systems. It details specific methodologies for quantifying feature importance, generating rationales for individual predictions, a

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  • nvidia/model-optimizerNVIDIA avatar

    NVIDIA/Model-Optimizer

    5,554View on GitHub↗

    Model-Optimizer is a deep learning toolkit and framework dedicated to compressing, pruning, quantizing, and optimizing neural network architectures. It provides methodologies covering weight quantization, model distillation, and speculative decoding for efficient text generation, alongside automated neural architecture search for discovering optimal network structures. The library implements post-training quantization pipelines that convert high-precision neural network weights into lower-bit formats using calibration data. Additional optimization techniques include teacher-student knowledge

    Python
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  • cs231n/cs231n.github.iocs231n avatar

    cs231n/cs231n.github.io

    10,923View on GitHub↗

    This project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum

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