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 401–450 of 1915 papers

TitleStatusHype
Carbon-Efficient Neural Architecture Search—0
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
Zero-Shot Neural Architecture Search: Challenges, Solutions, and OpportunitiesCode1
Neural Architecture Transfer 2: A Paradigm for Improving Efficiency in Multi-Objective Neural Architecture Search—0
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed SpacesCode1
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
Neural Fine-Tuning Search for Few-Shot LearningCode1
Flexible Channel Dimensions for Differentiable Architecture Search—0
Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learning—0
Rethink DARTS Search Space and Renovate a New BenchmarkCode0
Small Temperature is All You Need for Differentiable Architecture Search—0
Neural Architecture Design and Robustness: A Dataset—0
Happy People -- Image Synthesis as Black-Box Optimization Problem in the Discrete Latent Space of Deep Generative Models—0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—0
Generalizable Lightweight Proxy for Robust NAS against Diverse PerturbationsCode1
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
ColdNAS: Search to Modulate for User Cold-Start RecommendationCode1
Continuous Cartesian Genetic Programming based representation for Multi-Objective Neural Architecture SearchCode0
Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Search—0
LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity OptimizationCode1
Training-free Neural Architecture Search for RNNs and TransformersCode1
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence—0
FSD: Fully-Specialized Detector via Neural Architecture Search—0
DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion ModelsCode1
Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models—0
Meta-prediction Model for Distillation-Aware NAS on Unseen DatasetsCode1
Automated Search-Space Generation Neural Architecture Search—0
Enhancing Speech Emotion Recognition Through Differentiable Architecture SearchCode1
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
Searching a Compact Architecture for Robust Multi-Exposure Image FusionCode1
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
GeNAS: Neural Architecture Search with Better GeneralizationCode1
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
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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