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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
InstaNAS: Instance-aware Neural Architecture SearchCode0
Insights from the Use of Previously Unseen Neural Architecture Search DatasetsCode0
Inter-layer Transition in Neural Architecture SearchCode0
IRLAS: Inverse Reinforcement Learning for Architecture SearchCode0
CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive AutoencodersCode0
Butterfly Transform: An Efficient FFT Based Neural Architecture DesignCode0
ATOM: Attention Mixer for Efficient Dataset DistillationCode0
Building Optimal Neural Architectures using Interpretable KnowledgeCode0
Improving the sample-efficiency of neural architecture search with reinforcement learningCode0
Budgeted Training: Rethinking Deep Neural Network Training Under Resource ConstraintsCode0
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture SearchCode0
Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive TrainingCode0
Improving the Efficient Neural Architecture Search via Rewarding ModificationsCode0
Inner Ensemble Networks: Average Ensemble as an Effective RegularizerCode0
ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation FusionCode0
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020Code0
Improving Neural Architecture Search by Mixing a FireFly algorithm with a Training Free EvaluationCode0
Bridge the Gap Between Architecture Spaces via A Cross-Domain PredictorCode0
Improve Ranking Correlation of Super-net through Training Scheme from One-shot NAS to Few-shot NASCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network ArchitecturesCode0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image ClassificationCode0
Implantable Adaptive Cells: differentiable architecture search to improve the performance of any trained U-shaped networkCode0
Improved Differentiable Architecture Search for Language Modeling and Named Entity RecognitionCode0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution PruningCode0
Boosting Order-Preserving and Transferability for Neural Architecture Search: a Joint Architecture Refined Search and Fine-tuning ApproachCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
Bonsai-Net: One-Shot Neural Architecture Search via Differentiable PrunersCode0
How to 0wn NAS in Your Spare TimeCode0
AGNAS: Attention-Guided Micro- and Macro-Architecture SearchCode0
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of HyperparametersCode0
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass LensCode0
How to 0wn the NAS in Your Spare TimeCode0
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanismCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
IS-DARTS: Stabilizing DARTS through Precise Measurement on Candidate ImportanceCode0
BLOX: Macro Neural Architecture Search Benchmark and AlgorithmsCode0
Hierarchical Representations for Efficient Architecture SearchCode0
Blending Diverse Physical Priors with Neural NetworksCode0
A Genetic Programming Approach to Designing Convolutional Neural Network ArchitecturesCode0
Heterogeneous Graph Neural Architecture Search with GPT-4Code0
Hardware Aware Neural Network Architectures using FbNetCode0
Inexact bilevel stochastic gradient methods for constrained and unconstrained lower-level problemsCode0
Hardware/Software Co-Exploration of Neural ArchitecturesCode0
Autoequivariant Network Search via Group DecompositionCode0
ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation LearningCode0
Guided Evolution for Neural Architecture SearchCode0
A Semi-Decoupled Approach to Fast and Optimal Hardware-Software Co-Design of Neural AcceleratorsCode0
HiveNAS: Neural Architecture Search using Artificial Bee Colony OptimizationCode0
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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