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 451–500 of 1915 papers

TitleStatusHype
Efficient Visual Fault Detection for Freight Train via Neural Architecture Search with Data Volume Robustness—0
Automated Robustness with Adversarial Training as a Post-Processing Step—0
DDFAD: Dataset Distillation Framework for Audio Data—0
Automatic Routability Predictor Development Using Neural Architecture 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
Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration—0
Enabling NAS with Automated Super-Network Generation—0
Data Proxy Generation for Fast and Efficient Neural Architecture Search—0
Deep Demosaicing for Edge Implementation—0
Data-Free Neural Architecture Search via Recursive Label Calibration—0
Deep End2End Voxel2Voxel Prediction—0
DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification—0
Deep Learning Scaling is Predictable, Empirically—0
Efficient Transfer Learning via Joint Adaptation of Network Architecture and Weight—0
Deep Learning with Partially Labeled Data for Radio Map Reconstruction—0
Neural Epitome Search for Architecture-Agnostic Network Compression—0
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
AutoML for Multilayer Perceptron and FPGA Co-design—0
Deep Neural Network Architecture Search for Accurate Visual Pose Estimation aboard Nano-UAVs—0
AutoML Systems For Medical Imaging—0
Deep reinforcement learning in medical imaging: A literature review—0
Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
DEGAS: Differentiable Efficient Generator Search—0
An Approach for Efficient Neural Architecture Search Space Definition—0
Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy—0
Delta-NAS: Difference of Architecture Encoding for Predictor-based Evolutionary Neural Architecture Search—0
Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset—0
DASViT: Differentiable Architecture Search for Vision Transformer—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
Denoising Designs-inherited Search Framework for Image Denoising—0
DAS: Neural Architecture Search via Distinguishing Activation Score—0
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture—0
DARTS without a Validation Set: Optimizing the Marginal Likelihood—0
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey—0
Designing a Classifier for Active Fire Detection from Multispectral Satellite Imagery Using Neural Architecture Search—0
Designing deep neural networks for driver intention recognition—0
Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation—0
Efficient Traffic Classification using HW-NAS: Advanced Analysis and Optimization for Cybersecurity on Resource-Constrained Devices—0
Adaptive quantization with mixed-precision based on low-cost proxy—0
DARTS for Inverse Problems: a Study on Stability—0
DetOFA: Efficient Training of Once-for-All Networks for Object Detection Using Path Filter—0
Developing Convolutional Neural Networks using a Novel Lamarckian Co-Evolutionary Algorithm—0
Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS)—0
Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators—0
Is Differentiable Architecture Search truly a One-Shot Method?—0
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture—0
An Approach for Combining Multimodal Fusion and Neural Architecture Search Applied to Knowledge Tracing—0
DARTFormer: Finding The Best Type Of Attention—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