A data-driven initialization to population-based search strategies

[AAAI '23] PINAT: A Permutation INvariance Augmented Transformer for NAS Predictor

[ECCV2020] NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search

aw_nas: A Modularized and Extensible NAS Framework

Deep Bayesian Optimization for Problems with High-Dimensional Structure

Transforming Neural Architecture Search (NAS) into multi-objective optimization problems. A benchmark suite for testing evolutionary algorithms in deep learning.

[CVPRW22] Official PyTorch Implementation of "When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search"

[NeurIPS 2021] “Stronger NAS with Weaker Predictors“, Junru Wu, Xiyang Dai, Dongdong Chen, Yinpeng Chen, Mengchen Liu, Ye Yu, Zhangyang Wang, Zicheng Liu, Mei Chen and Lu Yuan

The official implementation of paper PreNAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search

Python implementation of "Unsupervised Domain Adaptive Graph Convolutional Networks", WWW-20.

An open source python library for non-linear piecewise symbolic regression based on Genetic Programming

Equation Learner, a neural network approach to symbolic regression

This repository contains code for paper SSNENAS.

Implementation of PGONAS for CVPR22W and RD-NAS for ICASSP23

Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)