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Found 6 papers, 3 papers with code
Hardware Aware Evolutionary Neural Architecture Search using Representation Similarity Metric
Our approach re-frames the neural architecture search problem as finding an architecture with performance similar to that of a reference model for a target hardware, while adhering to a cost constraint for that hardware.
Hardware Aware Neural Architecture Search
Neural Architecture Search
Impact of Disentanglement on Pruning Neural Networks
Deploying deep learning neural networks on edge devices, to accomplish task specific objectives in the real-world, requires a reduction in their memory footprint, power consumption, and latency.
Novelty Driven Evolutionary Neural Architecture Search
This can be reduced by using a supernet for estimating the fitness of an architecture due to weight sharing among all architectures in the search space.
Neural Architecture Search using Progressive Evolution
This can be reduced by using a supernet to estimate the fitness of every architecture in the search space due to its weight sharing nature.
Neural Architecture Search using Covariance Matrix Adaptation Evolution Strategy
Evolution-based neural architecture search requires high computational resources, resulting in long search time.
Evolving Neural Architecture Using One Shot Model
The architectures are represented by using the architecture parameter of the one shot model which results in the weight sharing among the architectures for a given population of architectures and also weight inheritance from one generation to the next generation of architectures.
Ranked #103 on
Neural Architecture Search
on ImageNet
