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 751–800 of 1915 papers

TitleStatusHype
Distilled Pruning: Using Synthetic Data to Win the LotteryCode0
LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search—0
Dynamical Isometry based Rigorous Fair Neural Architecture Search—0
Neural Architecture Transfer 2: A Paradigm for Improving Efficiency in Multi-Objective Neural Architecture Search—0
AutoST: Training-free Neural Architecture Search for Spiking TransformersCode0
DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs—0
Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles—0
Balanced Mixture of SuperNets for Learning the CNN Pooling ArchitectureCode0
Searching for the Fakes: Efficient Neural Architecture Search for General Face Forgery Detection—0
Flexible Channel Dimensions for Differentiable Architecture Search—0
Small Temperature is All You Need for Differentiable Architecture Search—0
Rethink DARTS Search Space and Renovate a New BenchmarkCode0
Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learning—0
Happy People -- Image Synthesis as Black-Box Optimization Problem in the Discrete Latent Space of Deep Generative Models—0
Neural Architecture Design and Robustness: A Dataset—0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—0
Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-ExpertsCode0
AutoML Systems For Medical Imaging—0
Deep Learning with Partially Labeled Data for Radio Map Reconstruction—0
Continuous Cartesian Genetic Programming based representation for Multi-Objective Neural Architecture SearchCode0
Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Search—0
FSD: Fully-Specialized Detector via Neural Architecture Search—0
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence—0
Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models—0
Automated Search-Space Generation Neural Architecture Search—0
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML—0
Do Not Train It: A Linear Neural Architecture Search of Graph Neural NetworksCode0
NAS-FM: Neural Architecture Search for Tunable and Interpretable Sound Synthesis based on Frequency Modulation—0
ALT: An Automatic System for Long Tail Scenario Modeling—0
Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactionsCode0
Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation—0
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems—0
Backpropagation-Free 4D Continuous Ant-Based Neural Topology SearchCode0
GPT-NAS: Evolutionary Neural Architecture Search with the Generative Pre-Trained Model—0
MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization—0
RATs-NAS: Redirection of Adjacent Trails on GCN for Neural Architecture Search—0
Symbolic Regression on FPGAs for Fast Machine Learning Inference—0
Neural Architecture Search for Intel Movidius VPU—0
Learning Language-Specific Layers for Multilingual Machine Translation—0
A Survey on Dataset Distillation: Approaches, Applications and Future Directions—0
Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresence—0
LayerNAS: Neural Architecture Search in Polynomial Complexity—0
QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model—0
SSS3D: Fast Neural Architecture Search For Efficient Three-Dimensional Semantic Segmentation—0
Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks—0
Neural Architecture Search for Visual Anomaly SegmentationCode0
AutoTaskFormer: Searching Vision Transformers for Multi-task Learning—0
DartsReNet: Exploring new RNN cells in ReNet architecturesCode0
Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search—0
Adversarially Robust Neural Architecture Search for Graph Neural Networks—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
5NARAccuracy (Test)46.66—Unverified
6ASE-NAS+Accuracy (Val)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