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 421–430 of 1915 papers

Title	Date	Tasks	Status
An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images	May 27, 2020	Neural Architecture Search	—Unverified
Additive regularization schedule for neural architecture search	Jun 18, 2024	Efficient Neural NetworkNeural Architecture Search	—Unverified
Auto-Meta: Automated Gradient Based Meta Learner Search	Jun 11, 2018	BIG-bench Machine LearningMeta-Learning	—Unverified
C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation	Dec 20, 2019	Image SegmentationMedical Image Segmentation	—Unverified
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity	Feb 3, 2019	Neural Architecture Search	—Unverified
Dynamical Isometry based Rigorous Fair Neural Architecture Search	Jul 5, 2023	FairnessNeural Architecture Search	—Unverified
Automatic Routability Predictor Development Using Neural Architecture Search	Dec 3, 2020	BIG-bench Machine LearningNeural Architecture Search	—Unverified
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution	Sep 20, 2019	Neural Architecture SearchTime Series	—Unverified
Automatic Network Adaptation for Ultra-Low Uniform-Precision Quantization	Dec 21, 2022	Neural Architecture SearchQuantization	—Unverified
Automatic Mixed-Precision Quantization Search of BERT	Dec 30, 2021	Knowledge DistillationModel Compression	—Unverified

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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