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

Showing 876900 of 1915 papers

TitleStatusHype
Deep Neural Network Architecture Search for Accurate Visual Pose Estimation aboard Nano-UAVs0
AutoML for Multilayer Perceptron and FPGA Co-design0
Neural Epitome Search for Architecture-Agnostic Network Compression0
AutoML for Large Capacity Modeling of Meta's Ranking Systems0
A Neural Architecture Search based Framework for Liquid State Machine Design0
DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network0
Deep Learning with Partially Labeled Data for Radio Map Reconstruction0
Deep Learning Scaling is Predictable, Empirically0
DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification0
AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting0
An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images0
Additive regularization schedule for neural architecture search0
Deep End2End Voxel2Voxel Prediction0
Deep Demosaicing for Edge Implementation0
Auto-Meta: Automated Gradient Based Meta Learner Search0
DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification0
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity0
DDFAD: Dataset Distillation Framework for Audio Data0
Automatic Routability Predictor Development Using Neural Architecture Search0
DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs0
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution0
DC-NAS: Divide-and-Conquer Neural Architecture Search0
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances0
DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation0
Automatic Network Adaptation for Ultra-Low Uniform-Precision Quantization0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPOS (ProxylessNAS (GPU) latency)Accuracy75.3Unverified
2SPOS (FBNet-C latency)Accuracy75.1Unverified
3SPOS (block search + channel search)Accuracy74.7Unverified
4MUXNet-xsTop-1 Error Rate33.3Unverified
5FBNetV2-F1Top-1 Error Rate31.7Unverified
6LayerNAS-60MTop-1 Error Rate31Unverified
7NASGEPTop-1 Error Rate29.51Unverified
8MUXNet-sTop-1 Error Rate28.4Unverified
9NN-MASS-ATop-1 Error Rate27.1Unverified
10FBNetV2-F3Top-1 Error Rate26.8Unverified
#ModelMetricClaimedVerifiedStatus
1CR-LSOAccuracy (Test)46.98Unverified
2Shapley-NASAccuracy (Test)46.85Unverified
3β-SDARTS-RSAccuracy (Test)46.71Unverified
4β-RDARTS-L2Accuracy (Test)46.71Unverified
5NARAccuracy (Test)46.66Unverified
6ASE-NAS+Accuracy (Val)46.66Unverified
7BaLeNAS-TFAccuracy (Test)46.54Unverified
8AG-NetAccuracy (Test)46.42Unverified
9Local searchAccuracy (Test)46.38Unverified
10NASBOTAccuracy (Test)46.37Unverified
#ModelMetricClaimedVerifiedStatus
1Balanced MixtureAccuracy (% )91.55Unverified
2GDASTop-1 Error Rate3.4Unverified
3Bonsai-NetTop-1 Error Rate3.35Unverified
4Net2 (2)Top-1 Error Rate3.3Unverified
5μDARTSTop-1 Error Rate3.28Unverified
6NN-MASS- CIFAR-CTop-1 Error Rate3.18Unverified
7DARTS (first order)Top-1 Error Rate3Unverified
8NN-MASS- CIFAR-ATop-1 Error Rate3Unverified
9AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82Unverified
10NASGEPTop-1 Error Rate2.82Unverified