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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 1201–1250 of 1915 papers

TitleStatusHype
Not All Operations Contribute Equally: Hierarchical Operation-Adaptive Predictor for Neural Architecture Search—0
Neural Architecture Search for Joint Human Parsing and Pose EstimationCode1
Learning Latent Architectural Distribution in Differentiable Neural Architecture Search via Variational Information Maximization—0
Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition—0
Neural Architecture Search via Combinatorial Multi-Armed Bandit—0
Weak NAS Predictor Is All You Need—0
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
Exploring single-path Architecture Search ranking correlations—0
Bractivate: Dendritic Branching in Segmentation Neural Architecture Search—0
Differentiable Graph Optimization for Neural Architecture Search—0
EnTranNAS: Towards Closing the Gap between the Architectures in Search and Evaluation—0
Toward Synergism in Macro Action EnsemblesCode0
Pareto-Frontier-aware Neural Architecture Search—0
Explicit Learning Topology for Differentiable Neural Architecture Search—0
Generative Adversarial Neural Architecture Search with Importance Sampling—0
Intra-layer Neural Architecture Search—0
FGNAS: FPGA-Aware Graph Neural Architecture Search—0
Neural Network Surgery: Combining Training with Topology Optimization—0
Fast MNAS: Uncertainty-aware Neural Architecture Search with Lifelong Learning—0
SEDONA: Search for Decoupled Neural Networks toward Greedy Block-wise LearningCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
NAHAS: Neural Architecture and Hardware Accelerator Search—0
Heterogeneous Model Transfer between Different Neural Networks—0
NASOA: Towards Faster Task-oriented Online Fine-tuning—0
Task-Agnostic and Adaptive-Size BERT Compression—0
TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture SearchCode1
Improving Zero-Shot Neural Architecture Search with Parameters Scoring—0
Efficient Graph Neural Architecture Search—0
Efficient Differentiable Neural Architecture Search with Model Parallelism—0
Searching for Convolutions and a More Ambitious NAS—0
SACoD: Sensor Algorithm Co-Design Towards Efficient CNN-powered Intelligent PhlatCamCode0
Uniform-Precision Neural Network Quantization via Neural Channel Expansion—0
HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark—0
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition—0
TRACE: Tensorizing and Generalizing Supernets from Neural Architecture Search—0
NASLib: A Modular and Flexible Neural Architecture Search Library—0
Don't be picky, all students in the right family can learn from good teachers—0
Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search—0
Memory-Efficient Hierarchical Neural Architecture Search for Image RestorationCode1
Evolving Neural Architecture Using One Shot ModelCode0
Learning by Self-Explanation, with Application to Neural Architecture Search—0
AutonoML: Towards an Integrated Framework for Autonomous Machine LearningCode0
Small-Group Learning, with Application to Neural Architecture SearchCode0
Discovering Robust Convolutional Architecture at Targeted Capacity: A Multi-Shot Approach—0
Searching for Controllable Image Restoration Networks—0
Resource-efficient DNNs for Keyword Spotting using Neural Architecture Search and QuantizationCode0
Improving the Efficient Neural Architecture Search via Rewarding ModificationsCode0
On the performance of deep learning for numerical optimization: an application to protein structure prediction—0
AutoCaption: Image Captioning with Neural Architecture Search—0
Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces—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