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

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Showing 1101–1150 of 1915 papers

TitleStatusHype
Proxyless Neural Architecture Adaptation for Supervised Learning and Self-Supervised Learning—0
Pruning Compact ConvNets for Efficient Inference—0
PSE-Net: Channel Pruning for Convolutional Neural Networks with Parallel-subnets Estimator—0
Pushing Joint Image Denoising and Classification to the Edge—0
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs—0
PVNAS: 3D Neural Architecture Search with Point-Voxel Convolution—0
PV-NAS: Practical Neural Architecture Search for Video Recognition—0
Pyramid Architecture Search for Real-Time Image Deblurring—0
QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks—0
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles—0
Quantum Architecture Search with Unsupervised Representation Learning—0
Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization—0
Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation—0
Qubit-Wise Architecture Search Method for Variational Quantum Circuits—0
QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model—0
RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search—0
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning—0
Ranking Architectures by Feature Extraction Capabilities—0
Ranking architectures using meta-learning—0
Ranking Convolutional Architectures by their Feature Extraction Capabilities—0
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale—0
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking—0
RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving—0
RAPDARTS: Resource-Aware Progressive Differentiable Architecture Search—0
Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization—0
RATs-NAS: Redirection of Adjacent Trails on GCN for Neural Architecture Search—0
RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection—0
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS—0
Real-time Federated Evolutionary Neural Architecture Search—0
Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation—0
Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components—0
RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively—0
Reducing Inference Latency with Concurrent Architectures for Image Recognition—0
Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering—0
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search—0
Regularizing Differentiable Architecture Search with Smooth Activation—0
Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness—0
Reinforcement Learning with Chromatic Networks for Compact Architecture Search—0
Reinforcement Learning with Chromatic Networks—0
RENAS: Reinforced Evolutionary Neural Architecture Search—0
ResBuilder: Automated Learning of Depth with Residual Structures—0
Resizable Neural Networks—0
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search—0
Resource-aware Mixed-precision Quantization for Enhancing Deployability of Transformers for Time-series Forecasting on Embedded FPGAs—0
Resource-Aware Pareto-Optimal Automated Machine Learning Platform—0
Resource-Efficient Neural Architect—0
Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation—0
Rethinking Co-design of Neural Architectures and Hardware Accelerators—0
Rethinking the Number of Channels for the Convolutional Neural Network—0
Retinal Vessel Segmentation via Neuron Programming—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