Weight-sharing neural architecture search: A battle to shrink the optimization gap
Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …
individual search methods have been replaced by weight-sharing search methods for higher …
[HTML][HTML] Neural architecture search: A contemporary literature review for computer vision applications
M Poyser, TP Breckon - Pattern Recognition, 2024 - Elsevier
Abstract Deep Neural Networks have received considerable attention in recent years. As the
complexity of network architecture increases in relation to the task complexity, it becomes …
complexity of network architecture increases in relation to the task complexity, it becomes …
Ista-nas: Efficient and consistent neural architecture search by sparse coding
Neural architecture search (NAS) aims to produce the optimal sparse solution from a high-
dimensional space spanned by all candidate connections. Current gradient-based NAS …
dimensional space spanned by all candidate connections. Current gradient-based NAS …
Sphynx: A deep neural network design for private inference
Sphynx: A Deep Neural Network Design for Private Inference Page 1 22 September/October
2022 Copublished by the IEEE Computer and Reliability Societies 1540-7993/22©2022IEEE …
2022 Copublished by the IEEE Computer and Reliability Societies 1540-7993/22©2022IEEE …
Single-path mobile automl: Efficient convnet design and nas hyperparameter optimization
Can we reduce the search cost of Neural Architecture Search (NAS) from days down to only
a few hours? NAS methods automate the design of Convolutional Networks (ConvNets) …
a few hours? NAS methods automate the design of Convolutional Networks (ConvNets) …
Progressive feature interaction search for deep sparse network
Deep sparse networks (DSNs), of which the crux is exploring the high-order feature
interactions, have become the state-of-the-art on the prediction task with high-sparsity …
interactions, have become the state-of-the-art on the prediction task with high-sparsity …
Neural architecture search as sparse supernet
This paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-
Path and Multi-Path Search to automated Mixed-Path Search. In particular, we model the …
Path and Multi-Path Search to automated Mixed-Path Search. In particular, we model the …
Neural-architecture-search-based multiobjective cognitive automation system
Currently, deep-learning-based cognitive automation for decision-making in industrial
informatics is a new hot topic in the field of cognitive computing, among which multiobjective …
informatics is a new hot topic in the field of cognitive computing, among which multiobjective …
Fisher task distance and its application in neural architecture search
We formulate an asymmetric (or non-commutative) distance between tasks based on Fisher
Information Matrices, called Fisher task distance. This distance represents the complexity of …
Information Matrices, called Fisher task distance. This distance represents the complexity of …
Task-aware neural architecture search
The design of handcrafted neural networks requires a lot of time and resources. Recent
techniques in Neural Architecture Search (NAS) have proven to be competitive or better than …
techniques in Neural Architecture Search (NAS) have proven to be competitive or better than …