Neural Architecture Search

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

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1601–1625 of 1915 papers

Title	Date	Tasks	Status
Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions	Jan 20, 2024	Deep LearningNeural Architecture Search	CodeCode Available
Cascade Bagging for Accuracy Prediction with Few Training Samples	Aug 12, 2021	Data AugmentationEnsemble Learning	CodeCode Available
Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture Search	Aug 27, 2023	Efficient Neural NetworkFairness	CodeCode Available
Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search	Mar 9, 2019	DecoderGPU	CodeCode Available
Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification	Feb 17, 2023	Graph ClassificationGraph structure learning	CodeCode Available
PaRT: Parallel Learning Towards Robust and Transparent AI	Jan 24, 2022	Continual LearningMulti-Task Learning	CodeCode Available
Learning to reinforcement learn for Neural Architecture Search	Nov 9, 2019	Meta-LearningNeural Architecture Search	CodeCode Available
Learning Transferable Architectures for Scalable Image Recognition	Jul 21, 2017	Classificationimage-classification	CodeCode Available
Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline	May 5, 2022	Gait RecognitionGait Recognition in the Wild	CodeCode Available
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models	Nov 24, 2024	Click-Through Rate PredictionNeural Architecture Search	CodeCode Available
Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative Gain	Aug 6, 2021	Learning-To-RankNeural Architecture Search	CodeCode Available
CARS: Continuous Evolution for Efficient Neural Architecture Search	Sep 11, 2019	GPUNeural Architecture Search	CodeCode Available
PASHA: Efficient HPO and NAS with Progressive Resource Allocation	Jul 14, 2022	BIG-bench Machine LearningHyperparameter Optimization	CodeCode Available
LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction	Jun 12, 2021	DiversityEnsemble Learning	CodeCode Available
PASTA: Neural Architecture Search for Anomaly Detection in Multivariate Time Series	Dec 9, 2024	Anomaly DetectionNeural Architecture Search	CodeCode Available
Less is More: Proxy Datasets in NAS approaches	Mar 14, 2022	Neural Architecture Search	CodeCode Available
Path-Level Network Transformation for Efficient Architecture Search	Jun 7, 2018	GPUimage-classification	CodeCode Available
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search	Jul 12, 2019	GPUNeural Architecture Search	CodeCode Available
AutoLC: Search Lightweight and Top-Performing Architecture for Remote Sensing Image Land-Cover Classification	May 11, 2022	DecoderLand Cover Classification	CodeCode Available
SEDONA: Search for Decoupled Neural Networks toward Greedy Block-wise Learning	Jan 1, 2021	Neural Architecture Search	CodeCode Available
Lifelong Learning with Searchable Extension Units	Mar 19, 2020	Lifelong learningNeural Architecture Search	CodeCode Available
Neural Architecture Search via Two Constant Shared Weights Initialisations	Feb 9, 2023	Neural Architecture Search	CodeCode Available
FTT-NAS: Discovering Fault-Tolerant Convolutional Neural Architecture	Mar 20, 2020	Neural Architecture SearchQuantization	CodeCode Available
From Xception to NEXcepTion: New Design Decisions and Neural Architecture Search	Dec 16, 2022	Image ClassificationNeural Architecture Search	CodeCode Available
Unleashing the Power of Gradient Signal-to-Noise Ratio for Zero-Shot NAS	Jan 1, 2023	Neural Architecture Search	CodeCode Available

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All datasets ImageNet NAS-Bench-201, ImageNet-16-120 CIFAR-10 NAS-Bench-201, CIFAR-100 NAS-Bench-201, CIFAR-10 CIFAR-10 Image Classification CIFAR-100 NATS-Bench Topology, CIFAR-10 NATS-Bench Topology, CIFAR-100 NATS-Bench Topology, ImageNet16-120 Food-101 NAS-Bench-101

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SPOS (ProxylessNAS (GPU) latency)	Accuracy	75.3	—	Unverified
2	SPOS (FBNet-C latency)	Accuracy	75.1	—	Unverified
3	SPOS (block search + channel search)	Accuracy	74.7	—	Unverified
4	MUXNet-xs	Top-1 Error Rate	33.3	—	Unverified
5	FBNetV2-F1	Top-1 Error Rate	31.7	—	Unverified
6	LayerNAS-60M	Top-1 Error Rate	31	—	Unverified
7	NASGEP	Top-1 Error Rate	29.51	—	Unverified
8	MUXNet-s	Top-1 Error Rate	28.4	—	Unverified
9	NN-MASS-A	Top-1 Error Rate	27.1	—	Unverified
10	FBNetV2-F3	Top-1 Error Rate	26.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CR-LSO	Accuracy (Test)	46.98	—	Unverified
2	Shapley-NAS	Accuracy (Test)	46.85	—	Unverified
3	β-RDARTS-L2	Accuracy (Test)	46.71	—	Unverified
4	β-SDARTS-RS	Accuracy (Test)	46.71	—	Unverified
5	ASE-NAS+	Accuracy (Val)	46.66	—	Unverified
6	NAR	Accuracy (Test)	46.66	—	Unverified
7	BaLeNAS-TF	Accuracy (Test)	46.54	—	Unverified
8	AG-Net	Accuracy (Test)	46.42	—	Unverified
9	Local search	Accuracy (Test)	46.38	—	Unverified
10	NASBOT	Accuracy (Test)	46.37	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Balanced Mixture	Accuracy (% )	91.55	—	Unverified
2	GDAS	Top-1 Error Rate	3.4	—	Unverified
3	Bonsai-Net	Top-1 Error Rate	3.35	—	Unverified
4	Net2 (2)	Top-1 Error Rate	3.3	—	Unverified
5	μDARTS	Top-1 Error Rate	3.28	—	Unverified
6	NN-MASS- CIFAR-C	Top-1 Error Rate	3.18	—	Unverified
7	NN-MASS- CIFAR-A	Top-1 Error Rate	3	—	Unverified
8	DARTS (first order)	Top-1 Error Rate	3	—	Unverified
9	NASGEP	Top-1 Error Rate	2.82	—	Unverified
10	AlphaX-1 (cutout NASNet)	Top-1 Error Rate	2.82	—	Unverified