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 16261650 of 1915 papers

TitleStatusHype
Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learning0
D'OH: Decoder-Only Random Hypernetworks for Implicit Neural Representations0
Domain Adaptation by Maximizing Population Correlation with Neural Architecture Search0
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks0
Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search0
Don't be picky, all students in the right family can learn from good teachers0
Double Oracle Neural Architecture Search for Game Theoretic Deep Learning Models0
SDQ: Stochastic Differentiable Quantization with Mixed Precision0
DQNAS: Neural Architecture Search using Reinforcement Learning0
Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networks0
SEAL: Searching Expandable Architectures for Incremental Learning0
Differentiable Mask for Pruning Convolutional and Recurrent Networks0
Differentiable Graph Optimization for Neural Architecture Search0
Searching a High-Performance Feature Extractor for Text Recognition Network0
AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System0
Dynamical Isometry based Rigorous Fair Neural Architecture Search0
Dynamic Cell Structure via Recursive-Recurrent Neural Networks0
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound0
Differentiable Feature Aggregation Search for Knowledge Distillation0
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound0
DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems0
A Memetic Algorithm based on Variational Autoencoder for Black-Box Discrete Optimization with Epistasis among Parameters0
Ecological Neural Architecture Search0
EcoNAS: Finding Proxies for Economical Neural Architecture Search0
Searching Efficient Model-guided Deep Network for Image Denoising0
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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