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 1101–1150 of 1915 papers

TitleStatusHype
Exploring Complicated Search Spaces with Interleaving-Free Sampling—0
The UniNAS framework: combining modules in arbitrarily complex configurations with argument trees—0
Data-Free Neural Architecture Search via Recursive Label Calibration—0
Training BatchNorm Only in Neural Architecture Search and Beyond—0
Progressive Feature Interaction Search for Deep Sparse Network—0
TNASP: A Transformer-based NAS Predictor with a Self-evolution Framework—0
Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search—0
Memory-efficient Patch-based Inference for Tiny Deep Learning—0
Graph Differentiable Architecture Search with Structure Learning—0
Improving Differentiable Architecture Search with a Generative Model—0
MAPLE: Microprocessor A Priori for Latency Estimation—0
Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition—0
MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection—0
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule—0
Anomaly-resistant Graph Neural Networks via Neural Architecture Search—0
Automatic Generation of Neural Architecture Search SpacesCode0
FBNetV5: Neural Architecture Search for Multiple Tasks in One Run—0
SuperShaper: Task-Agnostic Super Pre-training of BERT Models with Variable Hidden Dimensions—0
ShrinkNAS : Single-Path One-Shot Operator Exploratory Training for Transformer with Dynamic Space Shrinking—0
JMSNAS: Joint Model Split and Neural Architecture Search for Learning over Mobile Edge Networks—0
A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting—0
Searching for TrioNet: Combining Convolution with Local and Global Self-AttentionCode0
Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search—0
Towards One Shot Search Space Poisoning in Neural Architecture Search—0
Full-attention based Neural Architecture Search using Context Auto-regression—0
Learning from Mistakes -- A Framework for Neural Architecture SearchCode0
An Approach for Combining Multimodal Fusion and Neural Architecture Search Applied to Knowledge Tracing—0
Approximate Neural Architecture Search via Operation Distribution Learning—0
TND-NAS: Towards Non-differentiable Objectives in Progressive Differentiable NAS Framework—0
A Data-driven Approach to Neural Architecture Search InitializationCode0
AUTOKD: Automatic Knowledge Distillation Into A Student Architecture Family—0
RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms—0
Communication-Efficient Separable Neural Network for Distributed Inference on Edge Devices—0
Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problems—0
Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study—0
AUTOSUMM: Automatic Model Creation for Text Summarization—0
Guided Evolution for Neural Architecture SearchCode0
A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick—0
Learning Graph Representation of Person-specific Cognitive Processes from Audio-visual Behaviours for Automatic Personality Recognition—0
Differentiable NAS Framework and Application to Ads CTR PredictionCode0
BINAS: Bilinear Interpretable Neural Architecture SearchCode0
Towards a Robust Differentiable Architecture Search under Label Noise—0
ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies—0
DARTS for Inverse Problems: a Study on Stability—0
Growing Representation Learning—0
NeuralArTS: Structuring Neural Architecture Search with Type Theory—0
GradSign: Model Performance Inference with Theoretical InsightsCode0
DPNAS: Neural Architecture Search for Deep Learning with Differential PrivacyCode0
Improving the sample-efficiency of neural architecture search with reinforcement learningCode0
CONetV2: Efficient Auto-Channel Size Optimization for CNNsCode0
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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