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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 901–950 of 1915 papers

TitleStatusHype
DC-NAS: Divide-and-Conquer Neural Architecture Search—0
Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection—0
Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection—0
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures—0
Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search—0
HNAS-reg: hierarchical neural architecture search for deformable medical image registration—0
DDFAD: Dataset Distillation Framework for Audio Data—0
Relational Graph Neural Network Design via Progressive Neural Architecture Search—0
HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search—0
Automatic Routability Predictor Development Using Neural Architecture Search—0
How Does Supernet Help in Neural Architecture Search?—0
DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification—0
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor—0
How Powerful are Performance Predictors in Neural Architecture Search?—0
Efficient Neural Architecture Search with Performance Prediction—0
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models—0
Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search—0
How to Train Your Super-Net: An Analysis of Training Heuristics in Weight-Sharing NAS—0
Betty: An Automatic Differentiation Library for Multilevel Optimization—0
A Semi-Supervised Assessor of Neural Architectures—0
HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search—0
Efficient Neural Architecture Search via Parameters Sharing—0
Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation—0
Efficient Neural Architecture Search for Emotion Recognition—0
HW-SW Optimization of DNNs for Privacy-preserving People Counting on Low-resolution Infrared Arrays—0
Hybrid Composition with IdleBlock: More Efficient Networks for Image Recognition—0
BETANAS: BalancEd TrAining and selective drop for Neural Architecture Search—0
Hybrid-Quantum Neural Architecture Search for The Proximal Policy Optimization Algorithm—0
Hyperparameter Optimization in Machine Learning—0
Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery—0
A General-Purpose Transferable Predictor for Neural Architecture Search—0
A Primal-Dual Approach to Bilevel Optimization with Multiple Inner Minima—0
HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet—0
HyT-NAS: Hybrid Transformers Neural Architecture Search for Edge Devices—0
Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search—0
iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization—0
Continual Learning via Learning a Continual Memory in Vision Transformer—0
Identifying phase transitions in physical systems with neural networks: a neural architecture search perspective—0
iHAS: Instance-wise Hierarchical Architecture Search for Deep Learning Recommendation Models—0
ILASH: A Predictive Neural Architecture Search Framework for Multi-Task Applications—0
IMB-NAS: Neural Architecture Search for Imbalanced Datasets—0
ImmuNeCS: Neural Committee Search by an Artificial Immune System—0
BNAS:An Efficient Neural Architecture Search Approach Using Broad Scalable Architecture—0
Best Practices for Scientific Research on Neural Architecture Search—0
Efficient NAS with FaDE on Hierarchical Spaces—0
Deep reinforcement learning in medical imaging: A literature review—0
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator—0
ASAP: Architecture Search, Anneal and Prune—0
A General Method For Automatic Discovery of Powerful Interactions In Click-Through Rate Prediction—0
Efficient Multi-Objective Neural Architecture Search via Pareto Dominance-based Novelty Search—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