[ICLR2025] Kolmogorov-Arnold Transformer
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Updated
Mar 23, 2025 - Python
[ICLR2025] Kolmogorov-Arnold Transformer
ikan: many kan variants for every body
Simple, functional authorization library and role management for ruby
PyTorch implementation of QKAN "Quantum-inspired Kolmogorov-Arnold Network" https://arxiv.org/abs/2509.14026
This is the implementation of the paper Enhanced Photovoltaic Power Forecasting: An iTransformer and LSTM-Based Model Integrating Temporal and Covariate Interactions
A systematic review of Kolmogorov-Arnold Networks that bridges them with MLPs, highlights their parameter-efficient, interpretable edge-basis design, maps the open-source ecosystem, and offers a practical guide to choosing architectures.
High order and sparse layers in pytorch. Lagrange Polynomial, Piecewise Lagrange Polynomial, Piecewise Discontinuous Lagrange Polynomial (Chebyshev nodes) and Fourier Series layers of arbitrary order. Piecewise implementations could be thought of as a 1d grid (for each neuron) where each grid element is Lagrange polynomial. Both full connected a…
Example of replacing MLP with KAN in autoencoder(AE) and variational autoencoder(VAE)
Kolmogorov–Arnold Networks (KAN) in PyTorch
KAE : KAN-based AutoEncoder (AE, VAE, VQ-VAE, RVQ, etc.)
GAN implemented with KAN convolution
Development of interactive platforms for city modeling and digital twins using React and MapLibre
Implicit representation of various things using PyTorch and high order layers
Parametric differentiable curves with PyTorch for continuous embeddings, shape-restricted models, or KANs
Experiments in language interpolation with high order sparse neural networks
Neural Network Implicit Representation of Partial Differential Equations
Bottleneck KANConv for Unet
Baantu Research: Hybrid KAN-Transformer for investigating learnable activations in LLM reasoning. Built on nanochat by Andrej Karpathy.
Experiments on using Kolmogorov-Arnold Networks (KAN) on Graph Learning
Research project comparing Kolmogorov-Arnold Networks vs MLPs across chess engines, computer vision, and anomaly detection using Rust.
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