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 851–900 of 1915 papers

TitleStatusHype
DetOFA: Efficient Training of Once-for-All Networks for Object Detection Using Path Filter—0
AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks—0
An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development—0
AutoPV: Automatically Design Your Photovoltaic Power Forecasting Model—0
Designing deep neural networks for driver intention recognition—0
Designing a Classifier for Active Fire Detection from Multispectral Satellite Imagery Using Neural Architecture Search—0
Self-Programming Artificial Intelligence Using Code-Generating Language Models—0
A Design Space Study for LISTA and Beyond—0
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey—0
Design and Scaffolded Training of an Efficient DNN Operator for Computer Vision on the Edge—0
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture—0
Denoising Designs-inherited Search Framework for Image Denoising—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
A Novel Training Protocol for Performance Predictors of Evolutionary Neural Architecture Search Algorithms—0
A-DeepPixBis: Attentional Angular Margin for Face Anti-Spoofing—0
Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search—0
Delta-NAS: Difference of Architecture Encoding for Predictor-based Evolutionary Neural Architecture Search—0
Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy—0
DEGAS: Differentiable Efficient Generator Search—0
Auto-MVCNN: Neural Architecture Search for Multi-view 3D Shape Recognition—0
A New Deep Neural Architecture Search Pipeline for Face Recognition—0
Deep reinforcement learning in medical imaging: A literature review—0
AutoML Systems For Medical Imaging—0
A Neural Architecture Search Method using Auxiliary Evaluation Metric based on ResNet Architecture—0
A Deeper Look at Zero-Cost Proxies for Lightweight NAS—0
Deep Neural Network Architecture Search for Accurate Visual Pose Estimation aboard Nano-UAVs—0
AutoML for Multilayer Perceptron and FPGA Co-design—0
Neural Epitome Search for Architecture-Agnostic Network Compression—0
AutoML for Large Capacity Modeling of Meta's Ranking Systems—0
A Neural Architecture Search based Framework for Liquid State Machine Design—0
DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network—0
Deep Learning with Partially Labeled Data for Radio Map Reconstruction—0
Deep Learning Scaling is Predictable, Empirically—0
DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification—0
AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting—0
An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images—0
Additive regularization schedule for neural architecture search—0
Deep End2End Voxel2Voxel Prediction—0
Deep Demosaicing for Edge Implementation—0
Auto-Meta: Automated Gradient Based Meta Learner 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
DDFAD: Dataset Distillation Framework for Audio Data—0
Automatic Routability Predictor Development Using 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
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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