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 751–775 of 1915 papers

Title	Date	Tasks	Status	Score
BAM: Bottleneck Attention Module	Jul 17, 2018	Neural Architecture Search	CodeCode Available	5
Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razor	Dec 15, 2022	GPUHardware Aware Neural Architecture Search	CodeCode Available	5
Hardware/Software Co-Exploration of Neural Architectures	Jul 6, 2019	Neural Architecture SearchReinforcement Learning	CodeCode Available	5
DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy	Oct 16, 2021	Deep LearningNeural Architecture Search	CodeCode Available	5
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation	Jan 10, 2019	GPUimage-classification	CodeCode Available	5
3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification	Jan 12, 2021	ClassificationGeneral Classification	CodeCode Available	5
Combinatorial Bayesian Optimization using the Graph Cartesian Product	Feb 1, 2019	Bayesian OptimizationNeural Architecture Search	CodeCode Available	5
Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach	May 15, 2020	GPUNeural Architecture Search	CodeCode Available	5
ABC-Di: Approximate Bayesian Computation for Discrete Data	Oct 19, 2020	Neural Architecture Searchvalid	CodeCode Available	5
Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks	May 23, 2023	Neural Architecture Search	CodeCode Available	5
Guided Evolution for Neural Architecture Search	Oct 28, 2021	Neural Architecture Search	CodeCode Available	5
GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NAS	Nov 22, 2024	Neural Architecture Search	CodeCode Available	5
Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture	Jun 21, 2023	Image ClassificationNeural Architecture Search	CodeCode Available	5
AutoDispNet: Improving Disparity Estimation With AutoML	May 17, 2019	AutoMLBayesian Optimization	CodeCode Available	5
Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search	Apr 12, 2022	Neural Architecture SearchRelation	CodeCode Available	5
Autoequivariant Network Search via Group Decomposition	Apr 10, 2021	Inductive BiasNeural Architecture Search	CodeCode Available	5
Hardware Aware Neural Network Architectures using FbNet	Jun 17, 2019	BenchmarkingNeural Architecture Search	CodeCode Available	5
Implantable Adaptive Cells: differentiable architecture search to improve the performance of any trained U-shaped network	May 6, 2024	Image SegmentationMedical Image Segmentation	CodeCode Available	5
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators	Jul 13, 2023	Drug DiscoveryGraph Embedding	CodeCode Available	5
Backpropagation-Free 4D Continuous Ant-Based Neural Topology Search	May 11, 2023	Neural Architecture Search	CodeCode Available	5
GradSign: Model Performance Inference with Theoretical Insights	Oct 16, 2021	modelNeural Architecture Search	CodeCode Available	5
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search	Feb 7, 2022	Bayesian OptimizationComputational Efficiency	CodeCode Available	5
Distilled Pruning: Using Synthetic Data to Win the Lottery	Jul 7, 2023	Efficient Neural NetworkModel Compression	CodeCode Available	5
PreVIous: A Methodology for Prediction of Visual Inference Performance on IoT Devices	Dec 13, 2019	Neural Architecture Search	CodeCode Available	5
GraphNAS: Graph Neural Architecture Search with Reinforcement Learning	Apr 22, 2019	General ClassificationInductive Learning	CodeCode Available	5

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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