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 701–725 of 1915 papers

TitleStatusHype
Carbon-Efficient Neural Architecture Search—0
Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays—0
Boosting Share Routing for Multi-task Learning—0
Stretchable Cells Help DARTS Search Better—0
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence—0
Exploiting Operation Importance for Differentiable Neural Architecture Search—0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—0
EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS—0
Cartesian Genetic Programming Approach for Designing Convolutional Neural Networks—0
A Survey on Dataset Distillation: Approaches, Applications and Future Directions—0
A Graph Neural Architecture Search Approach for Identifying Bots in Social Media—0
Exploring Resiliency to Natural Image Corruptions in Deep Learning using Design Diversity—0
Exploring Shared Structures and Hierarchies for Multiple NLP Tasks—0
Exploring single-path Architecture Search ranking correlations—0
Generalization Guarantees for Neural Architecture Search with Train-Validation Split—0
Exploring the Manifold of Neural Networks Using Diffusion Geometry—0
Extensible and Efficient Proxy for Neural Architecture Search—0
End-to-end Keyword Spotting using Neural Architecture Search and Quantization—0
Boosting Network: Learn by Growing Filters and Layers via SplitLBI—0
CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework—0
Fairer and More Accurate Tabular Models Through NAS—0
Chain-structured neural architecture search for financial time series forecasting—0
Accelerate Intermittent Deep Inference—0
Farthest Greedy Path Sampling for Two-shot Recommender Search—0
Encoder-Decoder Neural Architecture Optimization for Keyword Spotting—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