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 701–750 of 1915 papers

TitleStatusHype
Carbon-Efficient Neural Architecture Search—0
Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays—0
A Survey on Multi-Objective Neural Architecture Search—0
A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities—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
GP-NAS-ensemble: a model for NAS Performance Prediction—0
EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS—0
Cartesian Genetic Programming Approach for Designing Convolutional Neural Networks—0
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach—0
Enhancing Convolutional Neural Networks with Higher-Order Numerical Difference Methods—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
Enhanced MRI Reconstruction Network using Neural Architecture Search—0
Enhanced Gradient for Differentiable Architecture Search—0
Extensible and Efficient Proxy for Neural Architecture Search—0
A Survey on Evolutionary Neural Architecture Search—0
Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers—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
Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model—0
Farthest Greedy Path Sampling for Two-shot Recommender Search—0
Boosting Share Routing for Multi-task Learning—0
AutoADR: Automatic Model Design for Ad Relevance—0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—0
FastBO: Fast HPO and NAS with Adaptive Fidelity Identification—0
Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation—0
Faster Discovery of Neural Architectures by Searching for Paths in a Large Model—0
BNAS-v2: Memory-efficient and Performance-collapse-prevented Broad Neural Architecture Search—0
Chimera: A Block-Based Neural Architecture Search Framework for Event-Based Object Detection—0
Fast MNAS: Uncertainty-aware Neural Architecture Search with Lifelong Learning—0
Fast Neural Architecture Construction using EnvelopeNets—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
GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning—0
Fast Task-Aware Architecture Inference—0
End-to-end Keyword Spotting using Neural Architecture Search and Quantization—0
Boosting Network: Learn by Growing Filters and Layers via SplitLBI—0
Accelerate Intermittent Deep Inference—0
FBNetV5: Neural Architecture Search for Multiple Tasks in One Run—0
FDNAS: Improving Data Privacy and Model Diversity in AutoML—0
Encoder-Decoder Neural Architecture Optimization for Keyword Spotting—0
Coarse-to-Fine Searching for Efficient Generative Adversarial Networks—0
FedAutoMRI: Federated Neural Architecture Search for MR Image Reconstruction—0
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing—0
Federated Neural Architecture Search with Model-Agnostic Meta Learning—0
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures—0
Evolutionary Neural Architecture Search for Retinal Vessel Segmentation—0
Show:102550
← PrevPage 15 of 39Next →
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