residual-network

Python functions to create N-dimensional mechanical linkages consisting of struts and joints similar to the Strandbeest by Theo Jansen. The scripts allow one to treat the mechanical network of linkages between the struts as a non-linear neural net, which can be optimized to perform different functions. I call this a Rigid Neural Net (RigNN)

neural-network optimization nonlinear pytorch mechanical-engineering residual-network strandbeest mechanical-linkages nonlinear-neural-network physics-demonstration rignn engineering-demonstration n-dimensional-mechanics

Updated Mar 16, 2023
Python

sefeoglu / adversarial_examples_parseval_net

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Parseval Networks and Adversarial Examples

neural-network residual-network robustness adversarial-learning adversarial-machine-learning adversarial-examples parseval-network

Updated May 31, 2024
Jupyter Notebook

deep-learning-algorithm / ResNet

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Implementation of ResNet series Algorithm

pytorch resnet residual-network residual-learning resnet-50 resnet-18 resnet-34 resnet-101 resnet-152 densetnet densetnet-121 densetnet-169 densenet-201 densenet-264

Updated Jul 25, 2024
Python

bharatmahaur / LeNo

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Code for paper: "Improved Residual Network Based on Norm-Preservation for Visual Recognition" https://doi.org/10.1016/j.neunet.2022.10.023

deep-neural-networks computer-vision deep-learning pytorch imagenet image-recognition resnet convolutional-neural-networks residual-network resnext gradient-flow visual-recognition wideresnet optimization-problem gradient-analysis norm-preservation gradient-norm leno-resnet optimization-stability

Updated Oct 31, 2022
Python

aaaastark / Accelerometer-Sensors-Analysis

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Time Series Analysis: Accelerometer Sensors of Object Inclination and Vibration. Time Series Analysis by using different (State of Art Models) Machine and Deep Learning. Recurent Neural Network with CuDNNLSTM Model, Convolutional Autoencoder, Residual Network (ResNet) and MobileNet Model.

Updated Nov 13, 2022

AndyVale / benchmarks_vnncomp

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A reorganized collection of benchmarks from VNNCOMP since 2022, divided into three categories: fully connected, convolutional, and residual networks. Each category is available as a submodule, allowing you to download individual categories or all of them at once.

neural-network neural-networks benchmarks residual-network fully-connected-network convolutional-network neural-network-verification vnncomp