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
Neural Architecture Search for Intel Movidius VPU—0
Learning Language-Specific Layers for Multilingual Machine Translation—0
A Survey on Dataset Distillation: Approaches, Applications and Future DirectionsCode0
Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresence—0
QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model—0
HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture SearchCode1
LayerNAS: Neural Architecture Search in Polynomial Complexity—0
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested SubspacesCode1
Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks—0
SSS3D: Fast Neural Architecture Search For Efficient Three-Dimensional Semantic Segmentation—0
Can GPT-4 Perform Neural Architecture Search?Code1
Neural Architecture Search for Visual Anomaly SegmentationCode0
AutoTaskFormer: Searching Vision Transformers for Multi-task Learning—0
Canvas: End-to-End Kernel Architecture Search in Neural NetworksCode1
AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary DetectionCode2
Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search—0
DartsReNet: Exploring new RNN cells in ReNet architecturesCode0
Adversarially Robust Neural Architecture Search for Graph Neural Networks—0
AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks—0
Robust Neural Architecture Search—0
Data Aware Neural Architecture SearchCode0
Self-Supervised learning for Neural Architecture Search (NAS)—0
Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy—0
Are Neural Architecture Search Benchmarks Well Designed? A Deeper Look Into Operation ImportanceCode0
Tetra-AML: Automatic Machine Learning via Tensor Networks—0
FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation—0
CP-CNN: Core-Periphery Principle Guided Convolutional Neural Network—0
Transfer-Once-For-All: AI Model Optimization for Edge—0
Efficient Neural Architecture Search for Emotion Recognition—0
OFA^2: A Multi-Objective Perspective for the Once-for-All Neural Architecture SearchCode0
DetOFA: Efficient Training of Once-for-All Networks for Object Detection Using Path Filter—0
Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms—0
ERSAM: Neural Architecture Search For Energy-Efficient and Real-Time Social Ambiance Measurement—0
ElasticViT: Conflict-aware Supernet Training for Deploying Fast Vision Transformer on Diverse Mobile DevicesCode1
Neural Architecture Search for Effective Teacher-Student Knowledge Transfer in Language Models—0
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanismCode0
Exploring Resiliency to Natural Image Corruptions in Deep Learning using Design Diversity—0
SpaceEvo: Hardware-Friendly Search Space Design for Efficient INT8 InferenceCode1
Continual Learning via Learning a Continual Memory in Vision Transformer—0
MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReIDCode1
HyT-NAS: Hybrid Transformers Neural Architecture Search for Edge Devices—0
DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural NetworkCode0
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video RecognitionCode0
Deep Neural Network Architecture Search for Accurate Visual Pose Estimation aboard Nano-UAVs—0
Structured Pruning for Deep Convolutional Neural Networks: A surveyCode4
FTSO: Effective NAS via First Topology Second Operator—0
A Little Bit Attention Is All You Need for Person Re-Identification—0
EvoPrompting: Language Models for Code-Level Neural Architecture Search—0
Full Stack Optimization of Transformer Inference: a Survey—0
DCLP: Neural Architecture Predictor with Curriculum Contrastive LearningCode0
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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-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