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 1801–1850 of 1915 papers

TitleStatusHype
DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification—0
FACETS: Efficient Once-for-all Object Detection via Constrained Iterative Search—0
Self-Supervised learning for Neural Architecture Search (NAS)—0
Fairer and More Accurate Tabular Models Through NAS—0
Self-supervised Neural Architecture Search—0
FAQS: Communication-efficient Federate DNN Architecture and Quantization Co-Search for personalized Hardware-aware Preferences—0
Farthest Greedy Path Sampling for Two-shot Recommender Search—0
UDC: Unified DNAS for Compressible TinyML Models—0
Multi-trial Neural Architecture Search with Lottery Tickets—0
UFO-BLO: Unbiased First-Order Bilevel Optimization—0
FastBO: Fast HPO and NAS with Adaptive Fidelity Identification—0
Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation—0
Faster Discovery of Neural Architectures by Searching for Paths in a Large Model—0
BNAS-v2: Memory-efficient and Performance-collapse-prevented Broad Neural Architecture Search—0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking—0
Fast MNAS: Uncertainty-aware Neural Architecture Search with Lifelong Learning—0
Fast Neural Architecture Construction using EnvelopeNets—0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
DDFAD: Dataset Distillation Framework for Audio Data—0
Ultrafast Photorealistic Style Transfer via Neural Architecture Search—0
Fast Task-Aware Architecture Inference—0
DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs—0
DC-NAS: Divide-and-Conquer Neural Architecture Search—0
A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends—0
FBNetV5: Neural Architecture Search for Multiple Tasks in One Run—0
FDNAS: Improving Data Privacy and Model Diversity in AutoML—0
DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation—0
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS—0
FedAutoMRI: Federated Neural Architecture Search for MR Image Reconstruction—0
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing—0
Federated Neural Architecture Search with Model-Agnostic Meta Learning—0
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures—0
DBsurf: A Discrepancy Based Method for Discrete Stochastic Gradient Estimation—0
FedNAS: Federated Deep Learning via Neural Architecture Search—0
FedorAS: Federated Architecture Search under system heterogeneity—0
FENAS: Flexible and Expressive Neural Architecture Search—0
Data Proxy Generation for Fast and Efficient Neural Architecture Search—0
FGNAS: FPGA-Aware Graph Neural Architecture Search—0
Field-wise Embedding Size Search via Structural Hard Auxiliary Mask Pruning for Click-Through Rate Prediction—0
Filter redistribution templates for iteration-lessconvolutional model reduction—0
Finding Competitive Network Architectures Within a Day Using UCT—0
Finding Fast Transformers: One-Shot Neural Architecture Search by Component Composition—0
A General-Purpose Transferable Predictor for Neural Architecture Search—0
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach—0
Fine-Grained Neural Architecture Search—0
Data-Free Neural Architecture Search via Recursive Label Calibration—0
Fine-Tuning DARTS for Image Classification—0
Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset—0
Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problems—0
Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks—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-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