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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

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Showing 1401–1425 of 1915 papers

TitleStatusHype
AdvantageNAS: Efficient Neural Architecture Search with Credit AssignmentCode0
Auto-MVCNN: Neural Architecture Search for Multi-view 3D Shape Recognition—0
Skillearn: Machine Learning Inspired by Humans' Learning Skills—0
DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and L_0 RegularizationCode0
Batch Group Normalization—0
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models—0
Automatic Routability Predictor Development Using Neural Architecture Search—0
Understanding and Exploring the Network with Stochastic ArchitecturesCode0
Adapting Neural Architectures Between DomainsCode0
Differentiable Neural Architecture Search in Equivalent Space with Exploration EnhancementCode0
Learning by Passing Tests, with Application to Neural Architecture Search—0
Optimizing the Neural Architecture of Reinforcement Learning AgentsCode0
Inter-layer Transition in Neural Architecture SearchCode0
ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition—0
Multi-objective Neural Architecture Search with Almost No Training—0
Bringing AI To Edge: From Deep Learning's Perspective—0
aw_nas: A Modularized and Extensible NAS framework—0
Efficient Sampling for Predictor-Based Neural Architecture Search—0
A Review of Recent Advances of Binary Neural Networks for Edge Computing—0
ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation—0
FP-NAS: Fast Probabilistic Neural Architecture Search—0
Continuous Ant-Based Neural Topology Search—0
BARS: Joint Search of Cell Topology and Layout for Accurate and Efficient Binary ARchitectures—0
Large Scale Neural Architecture Search with Polyharmonic Splines—0
Effective, Efficient and Robust Neural Architecture Search—0
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All datasetsImageNetNAS-Bench-201, ImageNet-16-120CIFAR-10NAS-Bench-201, CIFAR-100NAS-Bench-201, CIFAR-10CIFAR-10 Image ClassificationCIFAR-100NATS-Bench Topology, CIFAR-10NATS-Bench Topology, CIFAR-100NATS-Bench Topology, ImageNet16-120Food-101NAS-Bench-101

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPOS (ProxylessNAS (GPU) latency)Accuracy75.3—Unverified
2SPOS (FBNet-C latency)Accuracy75.1—Unverified
3SPOS (block search + channel search)Accuracy74.7—Unverified
4MUXNet-xsTop-1 Error Rate33.3—Unverified
5FBNetV2-F1Top-1 Error Rate31.7—Unverified
6LayerNAS-60MTop-1 Error Rate31—Unverified
7NASGEPTop-1 Error Rate29.51—Unverified
8MUXNet-sTop-1 Error Rate28.4—Unverified
9NN-MASS-ATop-1 Error Rate27.1—Unverified
10FBNetV2-F3Top-1 Error Rate26.8—Unverified
#ModelMetricClaimedVerifiedStatus
1CR-LSOAccuracy (Test)46.98—Unverified
2Shapley-NASAccuracy (Test)46.85—Unverified
3β-RDARTS-L2Accuracy (Test)46.71—Unverified
4β-SDARTS-RSAccuracy (Test)46.71—Unverified
5ASE-NAS+Accuracy (Val)46.66—Unverified
6NARAccuracy (Test)46.66—Unverified
7BaLeNAS-TFAccuracy (Test)46.54—Unverified
8AG-NetAccuracy (Test)46.42—Unverified
9Local searchAccuracy (Test)46.38—Unverified
10NASBOTAccuracy (Test)46.37—Unverified
#ModelMetricClaimedVerifiedStatus
1Balanced MixtureAccuracy (% )91.55—Unverified
2GDASTop-1 Error Rate3.4—Unverified
3Bonsai-NetTop-1 Error Rate3.35—Unverified
4Net2 (2)Top-1 Error Rate3.3—Unverified
5μDARTSTop-1 Error Rate3.28—Unverified
6NN-MASS- CIFAR-CTop-1 Error Rate3.18—Unverified
7NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
8DARTS (first order)Top-1 Error Rate3—Unverified
9NASGEPTop-1 Error Rate2.82—Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified