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 651–700 of 1915 papers

TitleStatusHype
FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames—0
A Hardware-Aware System for Accelerating Deep Neural Network Optimization—0
ERNAS: An Evolutionary Neural Architecture Search for Magnetic Resonance Image Reconstructions—0
FP-NAS: Fast Probabilistic Neural Architecture Search—0
EPNAS: Efficient Progressive Neural Architecture Search—0
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome—0
Breaking the Architecture Barrier: A Method for Efficient Knowledge Transfer Across Networks—0
Entropy-Driven Mixed-Precision Quantization for Deep Network Design—0
Branched Multi-Task Networks: Deciding What Layers To Share—0
A Survey on Neural Architecture Search—0
Entropic Score metric: Decoupling Topology and Size in Training-free NAS—0
EnTranNAS: Towards Closing the Gap between the Architectures in Search and Evaluation—0
Brain development dictates energy constraints on neural architecture search: cross-disciplinary insights on optimization strategies—0
Enhancing Once-For-All: A Study on Parallel Blocks, Skip Connections and Early Exits—0
ERSAM: Neural Architecture Search For Energy-Efficient and Real-Time Social Ambiance Measurement—0
A Survey on Neural Architecture Search Based on Reinforcement Learning—0
Evaluating a Novel Neuroevolution and Neural Architecture Search System—0
Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks—0
Evaluating the Practicality of Learned Image Compression—0
Bringing AI To Edge: From Deep Learning's Perspective—0
Bractivate: Dendritic Branching in Segmentation Neural Architecture Search—0
A Survey on Multi-Objective 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
A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities—0
Evolutionary Algorithms in Approximate Computing: A Survey—0
Evolutionary Architecture Search For Deep Multitask Networks—0
Asynchronous Evolution of Deep Neural Network Architectures—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
Evolutionary Neural Architecture Search Supporting Approximate Multipliers—0
Evolutionary Neural Architecture Search for Image Restoration—0
Enhanced MRI Reconstruction Network using Neural Architecture Search—0
Evolutionary Neural Architecture Search for 3D Point Cloud Analysis—0
Enhanced Gradient for Differentiable 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
Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model—0
Boosting Share Routing for Multi-task Learning—0
Evolving Multi-Resolution Pooling CNN for Monaural Singing Voice Separation—0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—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
Forecasting of COVID-19 Cases, Using an Evolutionary Neural Architecture Search Approach—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
EvoPrompting: Language Models for Code-Level Neural Architecture Search—0
Exascale Deep Learning to Accelerate Cancer Research—0
Encoder-Decoder Neural Architecture Optimization for Keyword Spotting—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