SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1151–1175 of 1915 papers

TitleStatusHype
Across-Task Neural Architecture Search via Meta Learning—0
On the Security Risks of AutoMLCode0
Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo—0
SuperShaper: Task-Agnostic Super Pre-training of BERT Models with Variable Hidden Dimensions—0
ZARTS: On Zero-order Optimization for Neural Architecture Search—0
Accelerating Multi-Objective Neural Architecture Search by Random-Weight Evaluation—0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking—0
Conceptual Expansion Neural Architecture Search (CENAS)—0
An Analysis of Super-Net Heuristics in Weight-Sharing NAS—0
Max and Coincidence Neurons in Neural Networks—0
Inexact bilevel stochastic gradient methods for constrained and unconstrained lower-level problemsCode0
DAAS: Differentiable Architecture and Augmentation Policy Search—0
FedNAS: Federated Deep Learning via Neural Architecture Search—0
Neural Architecture Search via Ensemble-based Knowledge Distillation—0
Personalized Neural Architecture Search for Federated Learning—0
NAS-Bench-Zero: A Large Scale Dataset for Understanding Zero-Shot Neural Architecture Search—0
ConCoDE: Hard-constrained Differentiable Co-Exploration Method for Neural Architectures and Hardware Accelerators—0
Picking up the pieces: separately evaluating supernet training and architecture selection—0
Ranking Convolutional Architectures by their Feature Extraction Capabilities—0
What to expect of hardware metric predictors in NAS—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
Tr-NAS: Memory-Efficient Neural Architecture Search with Transferred Blocks—0
A Novel Watermarking Framework for Ownership Verification of DNN Architectures—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
SUMNAS: Supernet with Unbiased Meta-Features for 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