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 901–950 of 1915 papers

TitleStatusHype
Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search—0
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor—0
How Does Supernet Help in Neural Architecture Search?—0
DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification—0
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity—0
HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search—0
Relational Graph Neural Network Design via Progressive Neural Architecture Search—0
DDFAD: Dataset Distillation Framework for Audio Data—0
Automatic Routability Predictor Development Using Neural Architecture Search—0
HNAS-reg: hierarchical neural architecture search for deformable medical image registration—0
Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search—0
HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking—0
HMCNAS: Neural Architecture Search using Hidden Markov Chains and Bayesian Optimization—0
How Powerful are Performance Predictors in Neural Architecture Search?—0
DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs—0
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution—0
DC-NAS: Divide-and-Conquer Neural Architecture Search—0
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances—0
DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation—0
Automatic Network Adaptation for Ultra-Low Uniform-Precision Quantization—0
Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales—0
Automatic Mixed-Precision Quantization Search of BERT—0
An Efficient NAS-based Approach for Handling Imbalanced Datasets—0
α DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling—0
Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search—0
Hybrid Composition with IdleBlock: More Efficient Networks for Image Recognition—0
A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification—0
HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices—0
Hyperparameter Optimization in Machine Learning—0
Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery—0
Heterogeneous Model Transfer between Different Neural Networks—0
Heterogeneous Learning Rate Scheduling for Neural Architecture Search on Long-Tailed Datasets—0
HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet—0
HyT-NAS: Hybrid Transformers Neural Architecture Search for Edge Devices—0
HEP-NAS: Towards Efficient Few-shot Neural Architecture Search via Hierarchical Edge Partitioning—0
iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization—0
Heed the Noise in Performance Evaluations in Neural Architecture Search—0
Heat Source Layout Optimization Using Automatic Deep Learning Surrogate and Multimodal Neighborhood Search Algorithm—0
DBsurf: A Discrepancy Based Method for Discrete Stochastic Gradient Estimation—0
ILASH: A Predictive Neural Architecture Search Framework for Multi-Task Applications—0
IMB-NAS: Neural Architecture Search for Imbalanced Datasets—0
ImmuNeCS: Neural Committee Search by an Artificial Immune System—0
Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search—0
HCS-TNAS: Hybrid Constraint-driven Semi-supervised Transformer-NAS for Ultrasound Image Segmentation—0
Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search—0
NeuroNAS: Enhancing Efficiency of Neuromorphic In-Memory Computing for Intelligent Mobile Agents through Hardware-Aware Spiking Neural Architecture Search—0
HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images—0
Hard Work Does Not Always Pay Off: Poisoning Attacks on Neural Architecture Search—0
Data Proxy Generation for Fast and Efficient Neural Architecture Search—0
Data-Free Neural Architecture Search via Recursive Label Calibration—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