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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

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Showing 951–1000 of 1915 papers

TitleStatusHype
Multi-objective optimization for Hardware-aware Neural Architecture Search—0
Multi-Pass Transformer for Machine Translation—0
Multi-path Neural Networks for On-device Multi-domain Visual Classification—0
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models—0
Multiple Population Alternate Evolution Neural Architecture Search—0
Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Search—0
Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks—0
Discovering Robust Convolutional Architecture at Targeted Capacity: A Multi-Shot Approach—0
Multi-Task Neural Architecture Search Using Architecture Embedding and Transfer Rank—0
Mutation is all you need—0
NADER: Neural Architecture Design via Multi-Agent Collaboration—0
NAHAS: Neural Architecture and Hardware Accelerator Search—0
NAS-ASDet: An Adaptive Design Method for Surface Defect Detection Network using Neural Architecture Search—0
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition—0
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy—0
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies—0
NAS-Bench-Zero: A Large Scale Dataset for Understanding Zero-Shot Neural Architecture Search—0
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search—0
NASB: Neural Architecture Search for Binary Convolutional Neural Networks—0
NAS-Cap: Deep-Learning Driven 3-D Capacitance Extraction with Neural Architecture Search and Data Augmentation—0
NAS-Count: Counting-by-Density with Neural Architecture Search—0
NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing—0
NAS-FM: Neural Architecture Search for Tunable and Interpretable Sound Synthesis based on Frequency Modulation—0
NASGEM: Neural Architecture Search via Graph Embedding Method—0
NasHD: Efficient ViT Architecture Performance Ranking using Hyperdimensional Computing—0
NASIB: Neural Architecture Search withIn Budget—0
NASI: Label- and Data-agnostic Neural Architecture Search at Initialization—0
NASirt: AutoML based learning with instance-level complexity information—0
NASLib: A Modular and Flexible Neural Architecture Search Library—0
NAS-NeRF: Generative Neural Architecture Search for Neural Radiance Fields—0
NASOA: Towards Faster Task-oriented Online Fine-tuning—0
NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models—0
NAS-PRNet: Neural Architecture Search generated Phase Retrieval Net for Off-axis Quantitative Phase Imaging—0
NASPY: Automated Extraction of Automated Machine Learning Models—0
NASS: Optimizing Secure Inference via Neural Architecture Search—0
NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition—0
NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search—0
NAX: Co-Designing Neural Network and Hardware Architecture for Memristive Xbar based Computing Systems—0
Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles—0
Neighborhood-Aware Neural Architecture Search—0
Neighbourhood Distillation: On the benefits of non end-to-end distillation—0
NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization—0
Network Architecture Search for Domain Adaptation—0
Network architecture search of X-ray based scientific applications—0
Network Space Search for Pareto-Efficient Spaces—0
Neural Architecture Adaptation for Object Detection by Searching Channel Dimensions and Mapping Pre-trained Parameters—0
Neural Architecture Codesign for Fast Bragg Peak Analysis—0
Neural Architecture Design and Robustness: A Dataset—0
Neural Architecture Optimization with Graph VAE—0
Neural Architecture Performance Prediction Using Graph Neural 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-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