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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 1651–1700 of 1915 papers

TitleStatusHype
Searching for A Robust Neural Architecture in Four GPU HoursCode1
Improving One-shot NAS by Suppressing the Posterior Fading—0
Fast and Practical Neural Architecture SearchCode0
Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification—0
Blending Diverse Physical Priors with Neural NetworksCode0
Sub-Architecture Ensemble Pruning in Neural Architecture SearchCode0
ReNAS:Relativistic Evaluation of Neural Architecture SearchCode1
A Quantile-based Approach for Hyperparameter Transfer Learning—0
Towards modular and programmable architecture searchCode0
Hierarchical Neural Architecture Search via Operator ClusteringCode0
Exascale Deep Learning to Accelerate Cancer Research—0
CNAS: Channel-Level Neural Architecture Search—0
Boosting Network: Learn by Growing Filters and Layers via SplitLBI—0
Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search—0
Neural Operator Search—0
Resizable Neural Networks—0
Scaling Up Neural Architecture Search with Big Single-Stage Models—0
BANANAS: Bayesian Optimization with Neural Networks for Neural Architecture Search—0
Filter redistribution templates for iteration-lessconvolutional model reduction—0
Deep Bayesian Structure NetworksCode0
VAENAS: Sampling Matters in Neural Architecture Search—0
Multi-objective Neural Architecture Search via Predictive Network Performance Optimization—0
Reinforcement Learning with Chromatic Networks—0
Evo-NAS: Evolutionary-Neural Hybrid Agent for Architecture Search—0
On Weight-Sharing and Bilevel Optimization in Architecture Search—0
Balanced One-shot Neural Architecture OptimizationCode0
Genetic Neural Architecture Search for automatic assessment of human sperm images—0
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution—0
Understanding Architectures Learnt by Cell-based Neural Architecture SearchCode0
Memory-Efficient Hierarchical Neural Architecture Search for Image DenoisingCode1
Pose Neural Fabrics SearchCode0
Neural Architecture Search for Class-incremental Learning—0
CARS: Continuous Evolution for Efficient Neural Architecture SearchCode0
Differentiable Mask for Pruning Convolutional and Recurrent Networks—0
Neural Architecture Search in Embedding Space—0
Auto-GNN: Neural Architecture Search of Graph Neural Networks—0
Distributed creation of Machine learning agents for Blockchain analysis—0
Efficient Automatic Meta Optimization Search for Few-Shot Learning—0
Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection—0
Best Practices for Scientific Research on Neural Architecture Search—0
Rethinking the Number of Channels for the Convolutional Neural Network—0
MANAS: Multi-Agent Neural Architecture Search—0
Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation—0
Neural Architecture Search for Joint Optimization of Predictive Power and Biological KnowledgeCode0
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research—0
HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking—0
Once-for-All: Train One Network and Specialize it for Efficient DeploymentCode1
On the Bounds of Function Approximations—0
Efficient Deep Neural Networks—0
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters—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