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

Showing 901925 of 1915 papers

TitleStatusHype
Relational Graph Neural Network Design via Progressive Neural Architecture Search0
DDFAD: Dataset Distillation Framework for Audio Data0
Automatic Routability Predictor Development Using Neural Architecture Search0
HNAS-reg: hierarchical neural architecture search for deformable medical image registration0
Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search0
HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking0
HMCNAS: Neural Architecture Search using Hidden Markov Chains and Bayesian Optimization0
DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs0
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution0
DC-NAS: Divide-and-Conquer Neural Architecture Search0
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances0
DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation0
Automatic Network Adaptation for Ultra-Low Uniform-Precision Quantization0
How Powerful are Performance Predictors in Neural Architecture Search?0
Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales0
Automatic Mixed-Precision Quantization Search of BERT0
An Efficient NAS-based Approach for Handling Imbalanced Datasets0
α DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling0
Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search0
A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification0
HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices0
Heterogeneous Model Transfer between Different Neural Networks0
Heterogeneous Learning Rate Scheduling for Neural Architecture Search on Long-Tailed Datasets0
HEP-NAS: Towards Efficient Few-shot Neural Architecture Search via Hierarchical Edge Partitioning0
Heed the Noise in Performance Evaluations in Neural Architecture Search0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPOS (ProxylessNAS (GPU) latency)Accuracy75.3Unverified
2SPOS (FBNet-C latency)Accuracy75.1Unverified
3SPOS (block search + channel search)Accuracy74.7Unverified
4MUXNet-xsTop-1 Error Rate33.3Unverified
5FBNetV2-F1Top-1 Error Rate31.7Unverified
6LayerNAS-60MTop-1 Error Rate31Unverified
7NASGEPTop-1 Error Rate29.51Unverified
8MUXNet-sTop-1 Error Rate28.4Unverified
9NN-MASS-ATop-1 Error Rate27.1Unverified
10FBNetV2-F3Top-1 Error Rate26.8Unverified
#ModelMetricClaimedVerifiedStatus
1CR-LSOAccuracy (Test)46.98Unverified
2Shapley-NASAccuracy (Test)46.85Unverified
3β-RDARTS-L2Accuracy (Test)46.71Unverified
4β-SDARTS-RSAccuracy (Test)46.71Unverified
5ASE-NAS+Accuracy (Val)46.66Unverified
6NARAccuracy (Test)46.66Unverified
7BaLeNAS-TFAccuracy (Test)46.54Unverified
8AG-NetAccuracy (Test)46.42Unverified
9Local searchAccuracy (Test)46.38Unverified
10NASBOTAccuracy (Test)46.37Unverified
#ModelMetricClaimedVerifiedStatus
1Balanced MixtureAccuracy (% )91.55Unverified
2GDASTop-1 Error Rate3.4Unverified
3Bonsai-NetTop-1 Error Rate3.35Unverified
4Net2 (2)Top-1 Error Rate3.3Unverified
5μDARTSTop-1 Error Rate3.28Unverified
6NN-MASS- CIFAR-CTop-1 Error Rate3.18Unverified
7NN-MASS- CIFAR-ATop-1 Error Rate3Unverified
8DARTS (first order)Top-1 Error Rate3Unverified
9NASGEPTop-1 Error Rate2.82Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82Unverified