https://github.com/muhanzhang/D-VAE
공식 코드D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
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/muhanzhang/DVAE
공식 코드D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
https://github.com/musslick/DARTS-Cognitive-Modeling
공식 코드https://github.com/nabk89/NAS-with-Proxy-data
공식 코드https://github.com/nabk89/autosnn
공식 코드https://github.com/namnguyen0510/CSNAS
공식 코드https://github.com/naszilla/bananas
공식 코드BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
https://github.com/naszilla/nas-encodings
공식 코드https://github.com/naszilla/naszilla
공식 코드BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
https://github.com/negrinho/deep_architect
공식 코드https://github.com/niccolocavagnero/freerea
공식 코드https://github.com/nick11roberts/XD
공식 코드https://github.com/nightstorm0909/EvNAS
공식 코드https://github.com/nightstorm0909/nevonas
공식 코드https://github.com/nightstorm0909/pevonas
공식 코드https://github.com/ntienvu/TW_NAS
공식 코드Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
https://github.com/nutellamok/demystifyingntk
공식 코드https://github.com/ofsoundof/dhp
공식 코드https://github.com/ondratybl/vkdnw
공식 코드Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights
https://github.com/ondrejbohdal/pasha
공식 코드PASHA: Efficient HPO and NAS with Progressive Resource Allocation
https://github.com/onizukalab/autognn_mcts
공식 코드Efficient and Explainable Graph Neural Architecture Search via Monte-Carlo Tree Search
https://github.com/open-mmlab/mmdetection/tree/master/mmdet/models/necks
공식 코드https://github.com/orangeuw/automl4etc
공식 코드AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic Classification
https://github.com/orashi/NSE_NAS
공식 코드https://github.com/otouat/gnnevaluationmetrics
공식 코드https://github.com/ozgurkara99/ISNAS-DIP
공식 코드ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior
https://github.com/pasalab/sagnas
공식 코드SA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph Neural Architecture Search
https://github.com/patrickzh/deepcore
공식 코드DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning
https://github.com/pawpuk/curvbim
공식 코드Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data Imbalance
https://github.com/pedrambakh/ec-nas-bench
공식 코드EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Search
https://github.com/peerdavid/conflicting-bundles
공식 코드Auto-tuning of Deep Neural Networks by Conflicting Layer Removal
https://github.com/peustr/nfts-public
공식 코드https://github.com/pfnet-research/einconv
공식 코드Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks
https://github.com/pholypeng/pnas-mot
공식 코드PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture Search
https://github.com/phython96/ARGNP
공식 코드https://github.com/phython96/GNAS-MP
공식 코드Rethinking Graph Neural Architecture Search from Message-passing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.