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 871–880 of 1915 papers

Title	Date	Tasks	Status	Hype
FBNetV5: Neural Architecture Search for Multiple Tasks in One Run	Nov 19, 2021	Classificationimage-classification	—Unverified	0
Rethinking Dilated Convolution for Real-time Semantic Segmentation	Nov 18, 2021	DecoderGPU	CodeCode Available	1
ShrinkNAS : Single-Path One-Shot Operator Exploratory Training for Transformer with Dynamic Space Shrinking	Nov 16, 2021	Neural Architecture Search	—Unverified	0
SuperShaper: Task-Agnostic Super Pre-training of BERT Models with Variable Hidden Dimensions	Nov 16, 2021	Neural Architecture Search	—Unverified	0
JMSNAS: Joint Model Split and Neural Architecture Search for Learning over Mobile Edge Networks	Nov 16, 2021	Neural Architecture Search	—Unverified	0
A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting	Nov 15, 2021	feature selectionNeural Architecture Search	—Unverified	0
Searching for TrioNet: Combining Convolution with Local and Global Self-Attention	Nov 15, 2021	Neural Architecture Search	CodeCode Available	0
Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search	Nov 15, 2021	GPUNeural Architecture Search	—Unverified	0
Full-attention based Neural Architecture Search using Context Auto-regression	Nov 13, 2021	Fine-Grained Image Recognitionimage-classification	—Unverified	0
Towards One Shot Search Space Poisoning in Neural Architecture Search	Nov 13, 2021	Neural Architecture Search	—Unverified	0

Show:10 25 50

← PrevPage 88 of 192Next →

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