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 401–425 of 1915 papers

TitleStatusHype
Self-Programming Artificial Intelligence Using Code-Generating Language Models—0
A Design Space Study for LISTA and Beyond—0
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture—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
Dynamical Isometry based Rigorous Fair Neural Architecture Search—0
Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey—0
A New Deep Neural Architecture Search Pipeline for Face Recognition—0
Auto-MVCNN: Neural Architecture Search for Multi-view 3D Shape Recognition—0
A Deeper Look at Zero-Cost Proxies for Lightweight NAS—0
AutoML Systems For Medical Imaging—0
A Neural Architecture Search Method using Auxiliary Evaluation Metric based on ResNet Architecture—0
Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search—0
AutoML for Multilayer Perceptron and FPGA Co-design—0
AutoML for Large Capacity Modeling of Meta's Ranking Systems—0
A Neural Architecture Search based Framework for Liquid State Machine Design—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
Auto-Meta: Automated Gradient Based Meta Learner Search—0
C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation—0
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks—0
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity—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