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 1501–1550 of 1915 papers

TitleStatusHype
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound—0
Efficient OCT Image Segmentation Using Neural Architecture Search—0
SOTERIA: In Search of Efficient Neural Networks for Private InferenceCode0
What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning—0
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search—0
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks—0
Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot StartCode0
On Adversarial Robustness: A Neural Architecture Search perspectiveCode0
BRP-NAS: Prediction-based NAS using GCNs—0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation—0
Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search—0
VINNAS: Variational Inference-based Neural Network Architecture Search—0
NASGEM: Neural Architecture Search via Graph Embedding Method—0
Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery—0
Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive RecommendationCode0
Self-supervised Neural Architecture Search—0
Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable Blocks for Image Classification—0
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction—0
Semi-discrete optimization through semi-discrete optimal transport: a framework for neural architecture searchCode0
Traditional and accelerated gradient descent for neural architecture searchCode0
NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
Neural Architecture Optimization with Graph VAE—0
Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition—0
Differentially-private Federated Neural Architecture SearchCode0
Fine-Tuning DARTS for Image Classification—0
Inner Ensemble Networks: Average Ensemble as an Effective RegularizerCode0
Differentiable Neural Architecture Transformation for Reproducible Architecture Improvement—0
Multi-fidelity Neural Architecture Search with Knowledge DistillationCode0
Optimal Transport Kernels for Sequential and Parallel Neural Architecture SearchCode0
Bonsai-Net: One-Shot Neural Architecture Search via Differentiable PrunersCode0
AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System—0
Knowledge Distillation: A Survey—0
Speedy Performance Estimation for Neural Architecture SearchCode0
Efficient Architecture Search for Continual Learning—0
Conditional Neural Architecture Search—0
AutoHAS: Efficient Hyperparameter and Architecture Search—0
UFO-BLO: Unbiased First-Order Bilevel Optimization—0
Learning to Rank Learning Curves—0
FBNetV3: Joint Architecture-Recipe Search using Predictor PretrainingCode0
Hyperparameter optimization with REINFORCE and Transformers—0
All in One Bad Weather Removal Using Architectural Search—0
SP-NAS: Serial-to-Parallel Backbone Search for Object DetectionCode0
GP-NAS: Gaussian Process Based Neural Architecture Search—0
MemNAS: Memory-Efficient Neural Architecture Search With Grow-Trim LearningCode0
A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions—0
DC-NAS: Divide-and-Conquer Neural Architecture Search—0
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass LensCode0
Evolutionary NAS with Gene Expression Programming of Cellular EncodingCode0
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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