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

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Showing 1301–1350 of 1915 papers

TitleStatusHype
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture SearchCode0
Neural Architecture Search of SPD Manifold NetworksCode1
μNAS: Constrained Neural Architecture Search for MicrocontrollersCode1
Task-Aware Neural Architecture SearchCode0
Hierarchical Neural Architecture Search for Deep Stereo MatchingCode1
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity MicrocontrollersCode1
Optimal Subarchitecture Extraction For BERTCode1
Neural Architecture Performance Prediction Using Graph Neural Networks—0
ABC-Di: Approximate Bayesian Computation for Discrete DataCode0
How Does Supernet Help in Neural Architecture Search?—0
AutoADR: Automatic Model Design for Ad Relevance—0
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse CodingCode1
Direct Federated Neural Architecture Search—0
Revisiting Neural Architecture Search—0
Multi-path Neural Networks for On-device Multi-domain Visual Classification—0
Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework—0
Automated Concatenation of Embeddings for Structured PredictionCode1
Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture SearchCode1
Smooth Variational Graph Embeddings for Efficient Neural Architecture SearchCode1
Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks—0
DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural NetworksCode1
LETI: Latency Estimation Tool and Investigation of Neural Networks inference on Mobile GPU—0
Stochastic analysis of heterogeneous porous material with modified neural architecture search (NAS) based physics-informed neural networks using transfer learning—0
Neighbourhood Distillation: On the benefits of non end-to-end distillation—0
Effective Regularization Through Loss-Function Metalearning—0
MS-RANAS: Multi-Scale Resource-Aware Neural Architecture SearchCode0
Once Quantized for All: Progressively Searching for Quantized Compact Models—0
A Surgery of the Neural Architecture Evaluators—0
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search—0
Disentangled Neural Architecture Search—0
Multi-Pass Transformer for Machine Translation—0
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models—0
AutoRC: Improving BERT Based Relation Classification Models via Architecture Search—0
Evolutionary Architecture Search for Graph Neural NetworksCode0
An Experimental Study of Weight Initialization and Weight Inheritance Effects on Neuroevolution—0
MSR-DARTS: Minimum Stable Rank of Differentiable Architecture SearchCode0
BNAS-v2: Memory-efficient and Performance-collapse-prevented Broad Neural Architecture Search—0
UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation—0
AutoML for Multilayer Perceptron and FPGA Co-design—0
RelativeNAS: Relative Neural Architecture Search via Slow-Fast LearningCode1
DANCE: Differentiable Accelerator/Network Co-Exploration—0
AutoKWS: Keyword Spotting with Differentiable Architecture Search—0
Binarized Neural Architecture Search for Efficient Object Recognition—0
S3NAS: Fast NPU-aware Neural Architecture Search MethodologyCode1
Understanding the wiring evolution in differentiable neural architecture searchCode1
DARTS-: Robustly Stepping out of Performance Collapse Without IndicatorsCode1
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS—0
Neural Architecture Search For Keyword Spotting—0
Boosting Share Routing for Multi-task Learning—0
A Novel Training Protocol for Performance Predictors of Evolutionary Neural Architecture Search Algorithms—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