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

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Showing 401–450 of 1915 papers

TitleStatusHype
A Design Space Study for LISTA and Beyond—0
BNAS:An Efficient Neural Architecture Search Approach Using Broad Scalable Architecture—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
Efficient Neural Architecture Search for Emotion Recognition—0
Efficient Novelty-Driven Neural Architecture Search—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
Efficient Model Performance Estimation via Feature Histories—0
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity—0
Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution—0
Efficient Multi-Objective Neural Architecture Search via Pareto Dominance-based Novelty Search—0
Automatic Routability Predictor Development Using Neural Architecture Search—0
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution—0
Automatic Network Adaptation for Ultra-Low Uniform-Precision Quantization—0
Automatic Mixed-Precision Quantization Search of BERT—0
An Efficient NAS-based Approach for Handling Imbalanced Datasets—0
α DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling—0
Efficient Model Adaptation for Continual Learning at the Edge—0
Efficient NAS with FaDE on Hierarchical Spaces—0
Efficient OCT Image Segmentation Using Neural Architecture Search—0
Efficient Differentiable Neural Architecture Search with Model Parallelism—0
Efficient Differentiable Neural Architecture Search with Meta Kernels—0
Efficient Evaluation Methods for Neural Architecture Search: A Survey—0
Automated Robustness with Adversarial Training as a Post-Processing Step—0
Data Proxy Generation for Fast and Efficient Neural Architecture Search—0
Data-Free Neural Architecture Search via Recursive Label Calibration—0
Efficient Deep Neural Networks—0
Efficient Few-Shot Neural Architecture Search by Counting the Number of Nonlinear Functions—0
Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification—0
DBsurf: A Discrepancy Based Method for Discrete Stochastic Gradient Estimation—0
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
Efficient Automatic Meta Optimization Search for Few-Shot Learning—0
DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—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