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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 1401–1450 of 1915 papers

TitleStatusHype
OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping—0
OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU—0
Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond—0
Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation—0
A Semi-Supervised Assessor of Neural Architectures—0
AdaNAS: Adaptively Post-processing with Self-supervised Neural Architecture Search for Ensemble Rainfall Forecasts—0
Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design—0
Optimization and Deployment of Deep Neural Networks for PPG-based Blood Pressure Estimation Targeting Low-power Wearables—0
Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification—0
Optimized Deployment of Deep Neural Networks for Visual Pose Estimation on Nano-drones—0
Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search—0
Towards Interpretable Physical-Conceptual Catchment-Scale Hydrological Modeling using the Mass-Conserving-Perceptron—0
CiMNet: Towards Joint Optimization for DNN Architecture and Configuration for Compute-In-Memory Hardware—0
Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach—0
Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices—0
What to expect of hardware metric predictors in NAS—0
Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search—0
ActNAS : Generating Efficient YOLO Models using Activation NAS—0
ASAP: Architecture Search, Anneal and Prune—0
Overcoming Multi-Model Forgetting—0
Exploring the Intersection between Neural Architecture Search and Continual Learning—0
A Review of Recent Advances of Binary Neural Networks for Edge Computing—0
NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms—0
Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification—0
Pareto-Frontier-aware Neural Architecture Search—0
Partial Connection Based on Channel Attention for Differentiable Neural Architecture Search—0
A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search—0
ZeroLM: Data-Free Transformer Architecture Search for Language Models—0
Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks—0
Towards Neural Architecture Search for Transfer Learning in 6G Networks—0
ParZC: Parametric Zero-Cost Proxies for Efficient NAS—0
A resource-efficient method for repeated HPO and NAS problems—0
Zero-Shot NAS via the Suppression of Local Entropy Decrease—0
Towards One Shot Search Space Poisoning in Neural Architecture Search—0
Towards Optimal Compression: Joint Pruning and Quantization—0
Towards Oracle Knowledge Distillation with Neural Architecture Search—0
PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search—0
Performance-Aware Mutual Knowledge Distillation for Improving Neural Architecture Search—0
Performance-Oriented Neural Architecture Search—0
Arch-LLM: Taming LLMs for Neural Architecture Generation via Unsupervised Discrete Representation Learning—0
Personalized Federated Instruction Tuning via Neural Architecture Search—0
Personalized Neural Architecture Search for Federated Learning—0
Picking up the pieces: separately evaluating supernet training and architecture selection—0
Towards Privacy-Preserving Neural Architecture Search—0
Architecture Search of Dynamic Cells for Semantic Video Segmentation—0
Neural Architecture Search by Estimation of Network Structure Distributions—0
Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation—0
Poisoning the Search Space in Neural Architecture Search—0
Poisson Process for Bayesian Optimization—0
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference—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
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
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