https://github.com/On-Point-RND/SeqNAS
공식 코드SeqNAS: Neural Architecture Search for Event Sequence Classification
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/PITT-JZ-COOP/Co-Explore-NAS
공식 코드https://github.com/PKU-DAIR/SGL
공식 코드PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm
https://github.com/PatrykChrabaszcz/Imagenet32_Scripts
공식 코드A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
https://github.com/Pattio/DeepSwarm
공식 코드DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence
https://github.com/PhysicsNAS/PhysicsNAS
공식 코드https://github.com/Project-MONAI/tutorials/tree/master/automl/DiNTS
공식 코드DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
https://github.com/QQTh4/NAS4Ultrasound
공식 코드SSHNN: Semi-Supervised Hybrid NAS Network for Echocardiographic Image Segmentation
https://github.com/QUVA-Lab/COMBO
공식 코드Combinatorial Bayesian Optimization using the Graph Cartesian Product
https://github.com/QingbeiGuo/DNAL
공식 코드Differentiable Neural Architecture Learning for Efficient Neural Network Design
https://github.com/RICE-EIC/HW-NAS-Bench
공식 코드HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark
https://github.com/Ren-Research/OneProxy
공식 코드One Proxy Device Is Enough for Hardware-Aware Neural Architecture Search
https://github.com/RobGeada/SpiderNet-NTKLRC
공식 코드SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metrics
https://github.com/RobGeada/bonsai-net-lite
공식 코드Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners
https://github.com/RodriguesGabriel/greenmachine
공식 코드GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NAS
https://github.com/RolandGao/RegSeg
공식 코드Rethinking Dilated Convolution for Real-time Semantic Segmentation
https://github.com/SLDGroup/ZiCo
공식 코드ZiCo: Zero-shot NAS via Inverse Coefficient of Variation on Gradients
https://github.com/SNAS-Series/SNAS-Series
공식 코드https://github.com/Sanghyun-Hong/How-to-0wn-NAS-in-Your-Spare-Time
공식 코드https://github.com/Somedaywilldo/BM-NAS
공식 코드https://github.com/StefanoGiacomelli/Vowel_phonemes_Analysis_and_Classification_by_means_of_OCON_rectifiers_Deep_Learning_Architectures
공식 코드The OCON model: an old but gold solution for distributable supervised classification
https://github.com/Sunshine-Ye/Beta-DARTS
공식 코드$β$-DARTS: Beta-Decay Regularization for Differentiable Architecture Search
https://github.com/TAMU-VITA/AutoGAN
공식 코드AutoGAN: Neural Architecture Search for Generative Adversarial Networks
https://github.com/TAMU-VITA/AutoSpeech
공식 코드AutoSpeech: Neural Architecture Search for Speaker Recognition
https://github.com/TAMU-VITA/FasterSeg
공식 코드FasterSeg: Searching for Faster Real-time Semantic Segmentation
https://github.com/THUMNLab/AutoGL
공식 코드https://github.com/THUMNLab/awesome-auto-graph-learning
공식 코드Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions
https://github.com/TimRoith/BregmanLearning
공식 코드https://github.com/TomVeniat/SANAS
공식 코드Stochastic Adaptive Neural Architecture Search for Keyword Spotting
https://github.com/UTokyo-ICEPP/multiml
공식 코드https://github.com/UTokyo-ICEPP/multiml_htautau
공식 코드https://github.com/VDIGPKU/OPANAS
공식 코드OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection
https://github.com/VICO-UoE/DatasetCondensation
공식 코드https://github.com/VITA-Group/Sandwich-Batch-Normalization
공식 코드Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity
https://github.com/VITA-Group/TENAS
공식 코드Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective
https://github.com/VITA-Group/WeakNAS
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.