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 1051–1100 of 1915 papers

TitleStatusHype
On Accelerating Edge AI: Optimizing Resource-Constrained Environments—0
On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis—0
Once for All: Train One Network and Specialize it for Efficient Deployment—0
Once Quantized for All: Progressively Searching for Quantized Compact Models—0
DARTS-PRIME: Regularization and Scheduling Improve Constrained Optimization in Differentiable NAS—0
ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting—0
One-Shot Neural Architecture Search with Network Similarity Directed Initialization for Pathological Image Classification—0
Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting—0
On Neural Architecture Search for Resource-Constrained Hardware Platforms—0
On the Bounds of Function Approximations—0
On the Communication Complexity of Decentralized Bilevel Optimization—0
On the performance of deep learning for numerical optimization: an application to protein structure prediction—0
On Weight-Sharing and Bilevel Optimization in Architecture Search—0
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
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
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
Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search—0
Overcoming Multi-Model Forgetting—0
NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms—0
Pareto-Frontier-aware Neural Architecture Search—0
Partial Connection Based on Channel Attention for Differentiable Neural Architecture Search—0
Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks—0
ParZC: Parametric Zero-Cost Proxies for Efficient NAS—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
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
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
POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator—0
POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique—0
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification—0
Powering One-shot Topological NAS with Stabilized Share-parameter Proxy—0
PredNAS: A Universal and Sample Efficient Neural Architecture Search Framework—0
PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search—0
Pretrained Hybrids with MAD Skills—0
Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud—0
Probabilistic Model-Based Dynamic Architecture Search—0
Probabilistic Neural Architecture Search—0
Progressive Feature Interaction Search for Deep Sparse Network—0
Provably Convergent Federated Trilevel Learning—0
ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies—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