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 501–525 of 1915 papers

TitleStatusHype
A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification—0
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration—0
Adaptive quantization with mixed-precision based on low-cost proxy—0
DARTS for Inverse Problems: a Study on Stability—0
Connection Sensitivity Matters for Training-free DARTS: From Architecture-Level Scoring to Operation-Level Sensitivity Analysis—0
Is Differentiable Architecture Search truly a One-Shot Method?—0
Differentiable Feature Aggregation Search for Knowledge Distillation—0
Differentiable Graph Optimization for Neural Architecture Search—0
Differentiable Mask for Pruning Convolutional and Recurrent Networks—0
An Approach for Combining Multimodal Fusion and Neural Architecture Search Applied to Knowledge Tracing—0
DARTFormer: Finding The Best Type Of Attention—0
DARC: Differentiable ARchitecture Compression—0
DANCE: Differentiable Accelerator/Network Co-Exploration—0
An Analysis of Super-Net Heuristics in Weight-Sharing NAS—0
EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition—0
DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search—0
Analyzing and Mitigating Interference in Neural Architecture Search—0
AutoKWS: Keyword Spotting with Differentiable Architecture Search—0
DAAS: Differentiable Architecture and Augmentation Policy Search—0
Accelerator-aware Neural Network Design using AutoML—0
Analyzing the Expected Hitting Time of Evolutionary Computation-based Neural Architecture Search Algorithms—0
EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning—0
Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoring—0
Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model—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-SDARTS-RSAccuracy (Test)46.71—Unverified
4β-RDARTS-L2Accuracy (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
7DARTS (first order)Top-1 Error Rate3—Unverified
8NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
9AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified
10NASGEPTop-1 Error Rate2.82—Unverified