Pytorch implementation of SIREN - Implicit Neural Representations with Periodic Activation Function
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Updated
Jul 28, 2023 - Python
Pytorch implementation of SIREN - Implicit Neural Representations with Periodic Activation Function
Rethinking Image Inpainting via a Mutual Encoder Decoder with Feature Equalizations. ECCV 2020 Oral
深度学习系统笔记,包含深度学习数学基础知识、神经网络基础部件详解、深度学习炼丹策略、模型压缩算法详解,以及如何实现深度学习推理框架实战。
Korean OCR Model Design(한글 OCR 모델 설계)
PyTorch implementation of Sinusodial Representation networks (SIREN)
[TCAD 2018] Code for “Design Space Exploration of Neural Network Activation Function Circuits”
ActTensor: Activation Functions for TensorFlow. https://pypi.org/project/ActTensor-tf/ Authors: Pouya Ardehkhani, Pegah Ardehkhani
An easy-to-use library for GLU (Gated Linear Units) and GLU variants in TensorFlow.
QReLU and m-QReLU: Two novel quantum activation functions for Deep Learning in TensorFlow, Keras, and PyTorch
3D visualization of common activation functions
A PyTorch implementation of funnel activation https://arxiv.org/pdf/2007.11824.pdf
Project Regarding Analysis and Implementation of https://arxiv.org/abs/1602.02068
Experiments using different activation functions.
hyper-sinh: An Accurate and Reliable Activation Function from Shallow to Deep Learning in TensorFlow, Keras, and PyTorch
The Deep Learning exercises provided in DataCamp
PyTorch reimplementation of the Smooth ReLU activation function proposed in the paper "Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations" [arXiv 2022].
Unofficial pytorch implementation of Piecewise Linear Unit dynamic activation function
PyTorch reimplementation of the paper "Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks" [ICLR 2020].
m-arcsinh: A Reliable and Efficient Function for Supervised Machine Learning (scikit-learn, TensorFlow, and Keras) and Feature Extraction (scikit-learn)
Code for the paper "A Self-Adaptive and Multiple Activation Function Neural Network for Facial Expression Recognition"
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