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 901–950 of 1915 papers

TitleStatusHype
Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture SearchCode0
NASA: Neural Architecture Search and Acceleration for Hardware Inspired Hybrid NetworksCode0
BASQ: Branch-wise Activation-clipping Search Quantization for Sub-4-bit Neural NetworksCode0
Neural Architectural Backdoors—0
OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping—0
Multi-Agent Automated Machine Learning—0
HQNAS: Auto CNN deployment framework for joint quantization and architecture search—0
FAQS: Communication-efficient Federate DNN Architecture and Quantization Co-Search for personalized Hardware-aware Preferences—0
Λ-DARTS: Mitigating Performance Collapse by Harmonizing Operation Selection among CellsCode0
BLOX: Macro Neural Architecture Search Benchmark and AlgorithmsCode0
Analyzing the Expected Hitting Time of Evolutionary Computation-based Neural Architecture Search Algorithms—0
RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule NetworksCode0
Equivariance-aware Architectural Optimization of Neural Networks—0
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds—0
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness—0
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies—0
Inference Latency Prediction at the Edge—0
POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique—0
Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search—0
Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model—0
Siamese-NAS: Using Trained Samples Efficiently to Find Lightweight Neural Architecture by Prior KnowledgeCode0
DARTFormer: Finding The Best Type Of Attention—0
IMB-NAS: Neural Architecture Search for Imbalanced Datasets—0
BayesFT: Bayesian Optimization for Fault Tolerant Neural Network ArchitectureCode0
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances—0
Towards Regression-Free Neural Networks for Diverse Compute Platforms—0
Searching a High-Performance Feature Extractor for Text Recognition Network—0
SpeedLimit: Neural Architecture Search for Quantized Transformer Models—0
NasHD: Efficient ViT Architecture Performance Ranking using Hyperdimensional Computing—0
Tiered Pruning for Efficient Differentialble Inference-Aware Neural Architecture Search—0
Automatic and effective discovery of quantum kernelsCode0
Tab2vox: CNN-Based Multivariate Multilevel Demand Forecasting Framework by Tabular-To-Voxel Image Conversion—0
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference—0
Generalization Properties of NAS under Activation and Skip Connection Search—0
EZNAS: Evolving Zero Cost Proxies For Neural Architecture Scoring—0
NAAP-440 Dataset and Baseline for Neural Architecture Accuracy PredictionCode0
3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane DetectionCode0
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noiseCode0
Neural Architecture Search for Improving Latency-Accuracy Trade-off in Split Computing—0
Bayesian Neural Network Language Modeling for Speech RecognitionCode0
FocusFormer: Focusing on What We Need via Architecture Sampler—0
Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)Code0
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey—0
Differentiable Architecture Search with Random Features—0
Field-wise Embedding Size Search via Structural Hard Auxiliary Mask Pruning for Click-Through Rate Prediction—0
ObfuNAS: A Neural Architecture Search-based DNN Obfuscation ApproachCode0
Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation—0
NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms—0
Partial Connection Based on Channel Attention for Differentiable Neural Architecture Search—0
Show:102550
← PrevPage 19 of 39Next →
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-SDARTS-RSAccuracy (Test)46.71—Unverified
4β-RDARTS-L2Accuracy (Test)46.71—Unverified
5NARAccuracy (Test)46.66—Unverified
6ASE-NAS+Accuracy (Val)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
7DARTS (first order)Top-1 Error Rate3—Unverified
8NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
9AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified
10NASGEPTop-1 Error Rate2.82—Unverified