Neural Architecture Search
780 papers with code • 26 benchmarks • 27 datasets
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
Benchmarks
These leaderboards are used to track progress in Neural Architecture Search
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Libraries
Use these libraries to find Neural Architecture Search models and implementationsDatasets
CIFAR-10
ImageNet
CIFAR-100
MNIST
Oxford 102 Flower
STL-10
Stanford Cars
DTD
Food-101
FGVC-Aircraft
Latest papers with no code
Qubit-Wise Architecture Search Method for Variational Quantum Circuits
Considering the noise level limit, one crucial aspect for quantum machine learning is to design a high-performing variational quantum circuit architecture with small number of quantum gates.
Neural Architecture Search using Particle Swarm and Ant Colony Optimization
A process known as Neural Architecture Search (NAS) may be applied to automatically evaluate a large number of such architectures.
G-EvoNAS: Evolutionary Neural Architecture Search Based on Network Growth
The process begins from a shallow network, grows and evolves, and gradually deepens into a complete network, reducing the search complexity in the global space.
Revisiting Learning-based Video Motion Magnification for Real-time Processing
Video motion magnification is a technique to capture and amplify subtle motion in a video that is invisible to the naked eye.
LeMo-NADe: Multi-Parameter Neural Architecture Discovery with LLMs
Building efficient neural network architectures can be a time-consuming task requiring extensive expert knowledge.
Adaptive quantization with mixed-precision based on low-cost proxy
It is critical to deploy complicated neural network models on hardware with limited resources.
Personalized Federated Instruction Tuning via Neural Architecture Search
Federated Instruction Tuning (FIT) has shown the ability to achieve collaborative model instruction tuning among massive data owners without sharing private data.
Optimized Deployment of Deep Neural Networks for Visual Pose Estimation on Nano-drones
Miniaturized autonomous unmanned aerial vehicles (UAVs) are gaining popularity due to their small size, enabling new tasks such as indoor navigation or people monitoring.
A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends
In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object detection, and image segmentation.
MSTAR: Multi-Scale Backbone Architecture Search for Timeseries Classification
Most of the previous approaches to Time Series Classification (TSC) highlight the significance of receptive fields and frequencies while overlooking the time resolution.
