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 1751–1800 of 1915 papers

TitleStatusHype
Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks—0
Evaluating the Practicality of Learned Image Compression—0
Searching to Sparsify Tensor Decomposition for N-ary Relational Data—0
Search Space Adaptation for Differentiable Neural Architecture Search in Image Classification—0
Search-time Efficient Device Constraints-Aware Neural Architecture Search—0
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation—0
Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization—0
Evolutionary Algorithm Enhanced Neural Architecture Search for Text-Independent Speaker Verification—0
Evolutionary Algorithms in Approximate Computing: A Survey—0
Evolutionary Architecture Search For Deep Multitask Networks—0
A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities—0
Neural Epitome Search for Architecture-Agnostic Network Compression—0
Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification—0
Evolutionary Neural Architecture Search Supporting Approximate Multipliers—0
Evolutionary Neural Architecture Search for Image Restoration—0
DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network—0
Evolutionary Neural Architecture Search for 3D Point Cloud Analysis—0
Deep Learning with Partially Labeled Data for Radio Map Reconstruction—0
Deep Learning Scaling is Predictable, Empirically—0
Evolutionary-Neural Hybrid Agents for Architecture Search—0
Evolution Meets Diffusion: Efficient Neural Architecture Generation—0
Evolution of Activation Functions: An Empirical Investigation—0
Evolving Multi-Resolution Pooling CNN for Monaural Singing Voice Separation—0
Search to Distill: Pearls are Everywhere but not the Eyes—0
A Graph Neural Architecture Search Approach for Identifying Bots in Social Media—0
Yoga Pose Classification Using Transfer Learning—0
DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification—0
EZNAS: Evolving Zero Cost Proxies For Neural Architecture Scoring—0
Evo-NAS: Evolutionary-Neural Hybrid Agent for Architecture Search—0
Deep End2End Voxel2Voxel Prediction—0
EvoPrompting: Language Models for Code-Level Neural Architecture Search—0
Exascale Deep Learning to Accelerate Cancer Research—0
ExperienceThinking: Constrained Hyperparameter Optimization based on Knowledge and Pruning—0
SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture Search via Large Language Models—0
Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays—0
Explicit Learning Topology for Differentiable Neural Architecture Search—0
Stretchable Cells Help DARTS Search Better—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
Exploring Complicated Search Spaces with Interleaving-Free Sampling—0
EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS—0
Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search—0
Deep Demosaicing for Edge Implementation—0
Exploring Resiliency to Natural Image Corruptions in Deep Learning using Design Diversity—0
Self Semi Supervised Neural Architecture Search for Semantic Segmentation—0
Exploring Shared Structures and Hierarchies for Multiple NLP Tasks—0
Exploring single-path Architecture Search ranking correlations—0
Exploring the Manifold of Neural Networks Using Diffusion Geometry—0
Self-supervised Cross-silo Federated Neural Architecture Search—0
Extensible and Efficient Proxy for Neural Architecture Search—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