https://github.com/Friedrich1006/ESNAC
공식 코드Learnable Embedding Space for Efficient Neural Architecture Compression
Papers with Code 아카이브
Neural Architecture Search
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Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures.
Image Credit : NAS with Reinforcement Learning
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/GraphNAS/GraphNAS-simple
공식 코드GraphNAS: Graph Neural Architecture Search with Reinforcement Learning
https://github.com/Guang000/Awesome-Dataset-Distillation
공식 코드A Survey on Dataset Distillation: Approaches, Applications and Future Directions
https://github.com/HKBU-HPML/CovidNet3D
공식 코드https://github.com/HayeonLee/MetaD2A
공식 코드Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets
https://github.com/HighwayWu/InpaintingForensics
공식 코드GIID-Net: Generalizable Image Inpainting Detection via Neural Architecture Search and Attention
https://github.com/HikariX/ZO-DARTS_PP
공식 코드ZO-DARTS++: An Efficient and Size-Variable Zeroth-Order Neural Architecture Search Algorithm
https://github.com/ICCV-5-EENA/EENA
공식 코드https://github.com/IlyaTrofimov/MF_NAS_KD
공식 코드Multi-fidelity Neural Architecture Search with Knowledge Distillation
https://github.com/IlzeAmandaA/ABCdiscrete
공식 코드https://github.com/Innixma/autogluon-benchmarking
공식 코드AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
https://github.com/JaminFong/DenseNAS
공식 코드Densely Connected Search Space for More Flexible Neural Architecture Search
https://github.com/JaminFong/EAT-NAS
공식 코드EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search
https://github.com/JasonZHM/CAE-ADMM
공식 코드CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive Autoencoders
https://github.com/JasonZHM/CAEP
공식 코드CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive Autoencoders
https://github.com/JiahuiYu/slimmable_networks
공식 코드AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
https://github.com/JingbiaoMei/BayesFTNA
공식 코드BayesFT: Bayesian Optimization for Fault Tolerant Neural Network Architecture
https://github.com/Kthyeon/fedsup
공식 코드Supernet Training for Federated Image Classification under System Heterogeneity
https://github.com/LARS-research/CSSE-DDI
공식 코드Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction
https://github.com/LGE-ARC-AdvancedAI/auptimizer
공식 코드Auptimizer -- an Extensible, Open-Source Framework for Hyperparameter Tuning
https://github.com/LKJacky/Differentiable-Model-Scaling
공식 코드https://github.com/LaVieEnRoseSMZ/OQA
공식 코드Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search
https://github.com/Lausannen/NAS-FCOS
공식 코드NAS-FCOS: Efficient Search for Object Detection Architectures
https://github.com/LiYingwei/AutoNL
공식 코드Neural Architecture Search for Lightweight Non-Local Networks
https://github.com/MILVLG/mmnas
공식 코드https://github.com/MIT-HAN-LAB/ProxylessNAS
공식 코드ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
https://github.com/MSU-MLSys-Lab/CATE
공식 코드CATE: Computation-aware Neural Architecture Encoding with Transformers
https://github.com/MSU-MLSys-Lab/arch2vec
공식 코드Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?
https://github.com/MaiqiVerse/AutoGNR
공식 코드Automated Heterogeneous Network learning with Non-Recursive Message Passing
https://github.com/MalongTech/research-fad
공식 코드Representation Sharing for Fast Object Detector Search and Beyond
https://github.com/Meituan-AutoML/DARTS-
공식 코드DARTS-: Robustly Stepping out of Performance Collapse Without Indicators
https://github.com/MiaoZhang0525/iDARTS
공식 코드iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients
https://github.com/MrtnMndt/meta-learning-CODEBRIM
공식 코드https://github.com/Multi-Objective-NAS/self-supervised-nas
공식 코드Pretraining Neural Architecture Search Controllers with Locality-based Self-Supervised Learning
https://github.com/NVlabs/unas
공식 코드UNAS: Differentiable Architecture Search Meets Reinforcement Learning
https://github.com/NinaMaz/NAS_RL_torch
공식 코드Optimizing the Neural Architecture of Reinforcement Learning Agents
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.