NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

Last update: Dec 05, 2022

Overview

NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

It is NEAT but the weights are trained with Natural Evolution Strategy and the weights are shared across genomes.

Requirements

numpy
cloudpickle

Install

pip install nesneat

Optional

matplotlib # To draw networks
mpi4py # For parallelization
gym # For examples

Usage

Check examples

PYTHONPATH="$(pwd):$PYTHONPATH" python examples/cartpole.py
# Or in parallel
PYTHONPATH="$(pwd):$PYTHONPATH" mpirun -np 2 python examples/cartpole.py

Owner

Göktuğ Karakaşlı

Computer Science MSc. and Electronics engineer working on Machine Learning, Robotics, Computer Vision.

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NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

Related tags

Overview

NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

Requirements

Install

Optional

Usage

Owner

Göktuğ Karakaşlı

Anti-UAV base on PaddleDetection

Interacting Two-Hand 3D Pose and Shape Reconstruction from Single Color Image (ICCV 2021)

Code for "Diffusion is All You Need for Learning on Surfaces"

Pip-package for trajectory benchmarking from "Be your own Benchmark: No-Reference Trajectory Metric on Registered Point Clouds", ECMR'21

Occlusion robust 3D face reconstruction model in CFR-GAN (WACV 2022)

Structural Constraints on Information Content in Human Brain States

Neural Network Libraries

Code release for Local Light Field Fusion at SIGGRAPH 2019

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Top #1 Submission code for the first https://alphamev.ai MEV competition with best AUC (0.9893) and MSE (0.0982).

codes for Self-paced Deep Regression Forests with Consideration on Ranking Fairness

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Vertex AI: Serverless framework for MLOPs (ESP / ENG)

Build tensorflow keras model pipelines in a single line of code. Created by Ram Seshadri. Collaborators welcome. Permission granted upon request.

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Official PyTorch Implementation of paper "Deep 3D Mask Volume for View Synthesis of Dynamic Scenes", ICCV 2021.

A PaddlePaddle implementation of STGCN with a few modifications in the model architecture in order to forecast traffic jam.

Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.

Navigating StyleGAN2 w latent space using CLIP

Official code for 'Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urban Driving Scenes'