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 501–550 of 1915 papers

TitleStatusHype
A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification—0
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
DARTS for Inverse Problems: a Study on Stability—0
Differentiable Architecture Search with Random Features—0
Is Differentiable Architecture Search truly a One-Shot Method?—0
Connection Sensitivity Matters for Training-free DARTS: From Architecture-Level Scoring to Operation-Level Sensitivity Analysis—0
An Approach for Combining Multimodal Fusion and Neural Architecture Search Applied to Knowledge Tracing—0
Evolutionary Architecture Search For Deep Multitask Networks—0
Differentiable Graph Optimization for Neural Architecture Search—0
Differentiable Mask for Pruning Convolutional and Recurrent Networks—0
DARTFormer: Finding The Best Type Of Attention—0
DARC: Differentiable ARchitecture Compression—0
DANCE: Differentiable Accelerator/Network Co-Exploration—0
An Analysis of Super-Net Heuristics in Weight-Sharing NAS—0
DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search—0
Analyzing and Mitigating Interference in Neural Architecture Search—0
AutoKWS: Keyword Spotting with Differentiable Architecture Search—0
DAAS: Differentiable Architecture and Augmentation Policy Search—0
Accelerator-aware Neural Network Design using AutoML—0
Evolutionary Algorithm Enhanced Neural Architecture Search for Text-Independent Speaker Verification—0
Evaluating the Practicality of Learned Image Compression—0
Analyzing the Expected Hitting Time of Evolutionary Computation-based Neural Architecture Search Algorithms—0
Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks—0
Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization—0
Evolutionary Algorithms in Approximate Computing: A Survey—0
Evolutionary Neural Architecture Search Supporting Approximate Multipliers—0
EZNAS: Evolving Zero Cost Proxies For Neural Architecture Scoring—0
CrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systems—0
AUTOKD: Automatic Knowledge Distillation Into A Student Architecture Family—0
Adaptive Neural Networks Using Residual Fitting—0
CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks—0
CP-CNN: Core-Periphery Principle Guided Convolutional Neural Network—0
AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching—0
Core-set Sampling for Efficient Neural Architecture Search—0
Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs—0
ERNAS: An Evolutionary Neural Architecture Search for Magnetic Resonance Image Reconstructions—0
Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures—0
AutoHAS: Efficient Hyperparameter and Architecture Search—0
AMLA: an AutoML frAmework for Neural Network Design—0
A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting—0
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome—0
Continuous Ant-Based Neural Topology Search—0
Auto-GNN: Neural Architecture Search of Graph Neural Networks—0
Continual Segment: Towards a Single, Unified and Non-forgetting Continual Segmentation Model of 143 Whole-body Organs in CT Scans—0
Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans—0
Accelerating Neural Architecture Exploration Across Modalities Using Genetic Algorithms—0
EPNAS: Efficient Progressive Neural Architecture Search—0
ERSAM: Neural Architecture Search For Energy-Efficient and Real-Time Social Ambiance Measurement—0
Enhancing Once-For-All: A Study on Parallel Blocks, Skip Connections and Early Exits—0
Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection—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