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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1701–1750 of 1915 papers

TitleStatusHype
Deep Bayesian Structure NetworksCode0
On Weight-Sharing and Bilevel Optimization in Architecture Search—0
VAENAS: Sampling Matters in Neural Architecture Search—0
Multi-objective Neural Architecture Search via Predictive Network Performance Optimization—0
Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search—0
Balanced One-shot Neural Architecture OptimizationCode0
Understanding Architectures Learnt by Cell-based Neural Architecture SearchCode0
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
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
Efficient Automatic Meta Optimization Search for Few-Shot Learning—0
Distributed creation of Machine learning agents for Blockchain analysis—0
Best Practices for Scientific Research on Neural Architecture Search—0
Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection—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
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research—0
Neural Architecture Search for Joint Optimization of Predictive Power and Biological KnowledgeCode0
HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking—0
On the Bounds of Function Approximations—0
Efficient Deep Neural Networks—0
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters—0
Neural Architecture Search by Estimation of Network Structure Distributions—0
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of HyperparametersCode0
SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture SearchCode0
Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural NetworksCode0
AutoGAN: Neural Architecture Search for Generative Adversarial NetworksCode0
SqueezeNAS: Fast neural architecture search for faster semantic segmentationCode0
MoGA: Searching Beyond MobileNetV3Code0
Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks—0
MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning—0
Efficient Novelty-Driven Neural Architecture Search—0
XferNAS: Transfer Neural Architecture Search—0
A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera—0
Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures—0
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture SearchCode0
Neural Epitome Search for Architecture-Agnostic Network Compression—0
Reinforcement Learning with Chromatic Networks for Compact Architecture Search—0
EPNAS: Efficient Progressive Neural Architecture Search—0
Hardware/Software Co-Exploration of Neural ArchitecturesCode0
Genetic Network Architecture SearchCode0
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture SearchCode0
Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter OptimizationCode0
Evolving Robust Neural Architectures to Defend from Adversarial AttacksCode0
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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