- 2024/05/18 How to Train a KAN Model on the Titanic Dataset for Kaggle
- 2024/05/15 What are the key concepts of Kolmogorov Arnold networks?
- 2024/05/15 Kolmogorov-Arnold Networks: A Comprehensive Guide to Neural Network Advancement
- 2024/05/11 Kolmogorov–Arnold Networks (KAN) Are About To Change The AI World Forever
- 2024/05/10 Kolmogorov-Arnold Networks (KANs) Might Change AI As We Know It, Forever
- 2024/05/04 Cracking the Neural Code: KANs and the Future of Explainable AI
- 2024/05/04 What is the new Neural Network Architecture?(KAN) Kolmogorov-Arnold Networks Explained
- 2024/05/02 A Simplified Explanation Of The New Kolmogorov-Arnold Network (KAN) from MIT
- 2023/07/21 Demystifying TensorFlow’s Sequential API and Functional API: A Comprehensive Guide
- 2023/03/26 The Universal Approximation Theorem
- 2019/06/25 Deep networks and the Kolmogorov–Arnold theorem
- 2024/05/02 GN⁺: Kolmogorov-Arnold 네트워크 개발
- Kolmogorov-Arnold Networks (KANs)
- Wiki: Kolmogorov–Arnold representation theorem
- Wiki: Kolmogorov–Arnold Network
- Kolmogorov Arnold Network
- Natural Learning
- Universal approximation theorem
- Max Tegmark, MIT
- Ziming Liu, MIT
- Andrey Kolmogorov, Moscow State University
- Vladimir Arnold, Moscow State University
- Biography of Andrey Kolmogorov
- "Andrey Kolmogorov. A universal genius." Yana Kinderknecht (Butko)
- The Universal Approximation Theorem of Neural Networks
- Why Neural Networks Can Learn Any Function | The Universal Approximation Theorem
- 2024 Wav-KAN: Wavelet Kolmogorov-Arnold Networks
- 2024 TKAN: Temporal Kolmogorov-Arnold Networks
- 2024 Kolmogorov-Arnold Networks (KANs) for Time Series Analysis
- 2024 KAN: Kolmogorov-Arnold Networks
- 2024 Natural Learning
- 2020 Error bounds for deep ReLU networks using the Kolmogorov–Arnold superposition theorem
- https://github.com/remigenet/TKAN - Temporal Kolmogorov-Arnold Networks
- https://github.com/akaashdash/kansformers - Kansformers: Transformers using KANs
- https://github.com/1ssb/torchkan - An easy to use PyTorch implementation of the Kolmogorov Arnold Network and a few novel variations
- https://github.com/ZiyaoLi/fast-kan - FastKAN: Very Fast Implementation of Kolmogorov-Arnold Networks (KAN)
- https://github.com/team-daniel/KAN - Implementation on how to use Kolmogorov-Arnold Networks (KANs) for classification and regression tasks
- https://github.com/riiswa/kanrl - Kolmogorov-Arnold Q-Network (KAQN) - KAN applied to Reinforcement learning, initial experiments
- https://github.com/AdityaNG/kan-gpt - The PyTorch implementation of Generative Pre-trained Transformers (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling
- https://github.com/ale93111/pykan_mnist - Kolmogorov Arnold Networks trained on MNIST
- https://github.com/GistNoesis/FusedFourierKAN - C++ and Cuda ops for fused FourierKAN
- https://github.com/GistNoesis/FourierKAN/ - Pytorch Layer for FourierKAN
- https://github.com/Blealtan/efficient-kan - An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN)
- https://github.com/riiswa/kanrl - Kolmogorov-Arnold Network for Reinforcement Leaning, initial experiments
- https://github.com/KindXiaoming/pykan
**Kolmogorov-Arnold Networks(KANets)**는 인공 신경망의 새로운 아키텍처입니다. 이는 기계 학습, 특히 시계열 데이터 처리에 효과적으로 사용될 수 있습니다.
주요 특징은 다음과 같습니다:
KANets는 최근 제안된 신경망 아키텍처로, 복잡한 시스템을 모델링하고 시계열 패턴을 효율적으로 학습할 수 있는 잠재력을 가지고 있습니다.