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 1201–1250 of 1915 papers

TitleStatusHype
Analyzing and Mitigating Interference in Neural Architecture Search—0
StressNAS: Affect State and Stress Detection Using Neural Architecture Search—0
iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization—0
Lightweight Monocular Depth with a Novel Neural Architecture Search Method—0
Design and Scaffolded Training of an Efficient DNN Operator for Computer Vision on the Edge—0
Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer—0
D-DARTS: Distributed Differentiable Architecture SearchCode0
Lessons from the Clustering Analysis of a Search Space: A Centroid-based Approach to Initializing NAS—0
Trends in Neural Architecture Search: Towards the Acceleration of Search—0
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search—0
RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving—0
Single-DARTS: Towards Stable Architecture SearchCode0
Evolutionary Algorithms in Approximate Computing: A Survey—0
Probeable DARTS with Application to Computational PathologyCode0
CONet: Channel Optimization for Convolutional Neural NetworksCode0
Is Differentiable Architecture Search truly a One-Shot Method?—0
Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive TrainingCode0
Cascade Bagging for Accuracy Prediction with Few Training SamplesCode0
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture SearchCode0
NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models—0
Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative GainCode0
Efficient Neural Architecture Search with Performance Prediction—0
WAS-VTON: Warping Architecture Search for Virtual Try-on Network—0
FLASH: Fast Neural Architecture Search with Hardware Optimization—0
AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models—0
Homogeneous Architecture Augmentation for Neural PredictorCode0
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN—0
Experiments on Properties of Hidden Structures of Sparse Neural NetworksCode0
μDARTS: Model Uncertainty-Aware Differentiable Architecture Search—0
LENS: Layer Distribution Enabled Neural Architecture Search in Edge-Cloud Hierarchies—0
A Novel Evolutionary Algorithm for Hierarchical Neural Architecture SearchCode0
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures—0
Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search—0
AutoBERT-Zero: Evolving BERT Backbone from Scratch—0
Neural Architecture Search using Covariance Matrix Adaptation Evolution Strategy—0
LANA: Latency Aware Network Acceleration—0
Core-set Sampling for Efficient Neural Architecture Search—0
Bag of Tricks for Neural Architecture Search—0
Does Form Follow Function? An Empirical Exploration of the Impact of Deep Neural Network Architecture Design on Hardware-Specific Acceleration—0
Uncertainty Modeling of Emerging Device-based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search—0
Mutation is all you need—0
CHASE: Robust Visual Tracking via Cell-Level Differentiable Neural Architecture SearchCode0
Exploring Robust Architectures for Deep Artificial Neural NetworksCode0
Poisoning the Search Space in Neural Architecture Search—0
AutoAdapt: Automated Segmentation Network Search for Unsupervised Domain Adaptation—0
NAX: Co-Designing Neural Network and Hardware Architecture for Memristive Xbar based Computing Systems—0
Bayesian Deep Learning Hyperparameter Search for Robust Function Mapping to Polynomials with Noise—0
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization—0
Connection Sensitivity Matters for Training-free DARTS: From Architecture-Level Scoring to Operation-Level Sensitivity Analysis—0
DARTS-PRIME: Regularization and Scheduling Improve Constrained Optimization in Differentiable NAS—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-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