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 551–600 of 1915 papers

TitleStatusHype
Automatic Network Adaptation for Ultra-Low Uniform-Precision Quantization—0
From Xception to NEXcepTion: New Design Decisions and Neural Architecture SearchCode0
A Study on the Intersection of GPU Utilization and CNN Inference—0
Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razorCode0
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification—0
Single Cell Training on Architecture Search for Image Denoising—0
HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance ScalingCode1
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture SearchCode0
GENNAPE: Towards Generalized Neural Architecture Performance EstimatorsCode0
AIO-P: Expanding Neural Performance Predictors Beyond Image ClassificationCode1
Entropy-Driven Mixed-Precision Quantization for Deep Network Design—0
PIDS: Joint Point Interaction-Dimension Search for 3D Point CloudCode0
GraphPNAS: Learning Distribution of Good Neural Architectures via Deep Graph Generative Models—0
Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networks—0
MPCViT: Searching for Accurate and Efficient MPC-Friendly Vision Transformer with Heterogeneous AttentionCode1
DAMO-YOLO : A Report on Real-Time Object Detection DesignCode4
NAS-LID: Efficient Neural Architecture Search with Local Intrinsic DimensionCode1
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach—0
Minimizing the Accumulated Trajectory Error to Improve Dataset DistillationCode1
α DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling—0
HiveNAS: Neural Architecture Search using Artificial Bee Colony OptimizationCode0
Revisiting Training-free NAS Metrics: An Efficient Training-based MethodCode0
Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS)—0
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural NetworksCode0
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search—0
Multi-Objective Evolutionary for Object Detection Mobile Architectures Search—0
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free GrammarsCode1
Deep neural network based on F-neurons and its learningCode0
Speeding up NAS with Adaptive Subset Selection—0
Saliency-Aware Neural Architecture Search—0
Bridge the Gap Between Architecture Spaces via A Cross-Domain PredictorCode0
Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search—0
Automated Dominative Subspace Mining for Efficient Neural Architecture SearchCode0
Search to Pass Messages for Temporal Knowledge Graph CompletionCode1
Hierarchical quantum circuit representations for neural architecture searchCode1
PredNAS: A Universal and Sample Efficient Neural Architecture Search Framework—0
Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture SearchCode0
NAS-PRNet: Neural Architecture Search generated Phase Retrieval Net for Off-axis Quantitative Phase Imaging—0
NASA: Neural Architecture Search and Acceleration for Hardware Inspired Hybrid NetworksCode0
BASQ: Branch-wise Activation-clipping Search Quantization for Sub-4-bit Neural NetworksCode0
Neural Architectural Backdoors—0
OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping—0
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face RecognitionCode1
Extensible Proxy for Efficient NASCode1
Multi-Agent Automated Machine Learning—0
FAQS: Communication-efficient Federate DNN Architecture and Quantization Co-Search for personalized Hardware-aware Preferences—0
HQNAS: Auto CNN deployment framework for joint quantization and architecture search—0
AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine TranslationCode1
Λ-DARTS: Mitigating Performance Collapse by Harmonizing Operation Selection among CellsCode0
BLOX: Macro Neural Architecture Search Benchmark and AlgorithmsCode0
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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