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 751–800 of 1915 papers

TitleStatusHype
i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender SystemsCode0
Neural Architecture Search for Speech Emotion Recognition—0
Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation—0
Nix-TTS: Lightweight and End-to-End Text-to-Speech via Module-wise DistillationCode2
AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement—0
Generalizing Few-Shot NAS with Gradient MatchingCode1
Robust and Energy-efficient PPG-based Heart-Rate Monitoring—0
Demystifying the Neural Tangent Kernel from a Practical Perspective: Can it be trusted for Neural Architecture Search without training?Code0
AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning—0
A Semi-Decoupled Approach to Fast and Optimal Hardware-Software Co-Design of Neural AcceleratorsCode0
An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development—0
EmotionNAS: Two-stream Neural Architecture Search for Speech Emotion Recognition—0
Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable DevicesCode1
U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture SearchCode0
PACE: A Parallelizable Computation Encoder for Directed Acyclic GraphsCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Progressive Subsampling for Oversampled Data -- Application to Quantitative MRICode0
Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot LearningCode1
Learning Where To Look -- Generative NAS is Surprisingly EfficientCode1
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications—0
On Redundancy and Diversity in Cell-based Neural Architecture SearchCode0
TAS: Ternarized Neural Architecture Search for Resource-Constrained Edge DevicesCode0
Less is More: Proxy Datasets in NAS approachesCode0
Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond—0
Towards Less Constrained Macro-Neural Architecture SearchCode1
Multi-trial Neural Architecture Search with Lottery Tickets—0
Evolutionary Neural Cascade Search across SupernetworksCode1
A^3D: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks—0
Adaptive Cross-Layer Attention for Image RestorationCode1
Improving the Energy Efficiency and Robustness of tinyML Computer Vision using Log-Gradient Input Images—0
LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language ModelsCode2
WPNAS: Neural Architecture Search by jointly using Weight Sharing and Predictor—0
Neural Architecture Search using Progressive EvolutionCode0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
β-DARTS: Beta-Decay Regularization for Differentiable Architecture SearchCode1
Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoring—0
A Primal-Dual Approach to Bilevel Optimization with Multiple Inner Minima—0
PaSca: a Graph Neural Architecture Search System under the Scalable ParadigmCode1
ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting—0
An Efficient End-to-End 3D Voxel Reconstruction based on Neural Architecture SearchCode0
Accelerating Neural Architecture Exploration Across Modalities Using Genetic Algorithms—0
A Hardware-Aware System for Accelerating Deep Neural Network Optimization—0
SUTD-PRCM Dataset and Neural Architecture Search Approach for Complex Metasurface Design—0
The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices—0
Mixed-Block Neural Architecture Search for Medical Image Segmentation—0
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search—0
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification—0
Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification—0
Neural Architecture Search for Dense Prediction Tasks in Computer Vision—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