Results 51 to 60 of about 6,198,210 (289)
Differentiable Neural Network Architecture Search
, 2023 The aim of this work is to propose a system for differentiable architecture search, which can be used for design of some neural network types. The work is based on the DARTS (Differentiable architecture search) approach and implements similar system in ...
Eichler, Vojtěch
core +1 more source
Gradient-Based Neural Architecture Search: A Comprehensive Evaluation
Machine Learning and Knowledge Extraction, 2023 One of the challenges in deep learning involves discovering the optimal architecture for a specific task. This is effectively tackled through Neural Architecture Search (NAS).
Sarwat Ali, M. Arif Wani
doaj +1 more source
A Survey on Neural Architecture Search
CoRR, 2019 The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of a wide variety of automated methods for neural architecture search. The choice of the network architecture has proven to be critical, and many advances in deep learning spring from its immediate improvements. However, deep learning
Martin Wistuba +2 more
openaire +3 more sources
Federated Neural Architecture Search [PDF]
, 2022 To preserve user privacy while enabling mobile intelligence, techniques have been proposed to train deep neural networks on decentralized data. However, training over decentralized data makes the design of neural architecture quite difficult as it ...
Bian, Kaigui +6 more
core +1 more source
Disentangled Neural Architecture Search
CoRR, 2020 Neural architecture search has shown its great potential in various areas recently. However, existing methods rely heavily on a black-box controller to search architectures, which suffers from the serious problem of lacking interpretability. In this paper, we propose disentangled neural architecture search (DNAS) which disentangles the hidden ...
Xinyue Zheng +3 more
openaire +2 more sources
Poisoning the Search Space in Neural Architecture Search
CoRR, 2021 All authors contributed equally. Appears in AdvML Workshop @ ICML2021: A Blessing in Disguise: The Prospects and Perils of Adversarial Machine ...
Robert Wu, Nayan Saxena, Rohan Jain
openaire +2 more sources
EGNAS: Efficient Graph Neural Architecture Search Through Evolutionary Algorithm
MathematicsThe primary objective of our research is to enhance the efficiency and effectiveness of Neural Architecture Search (NAS) with regard to Graph Neural Networks (GNNs).
Younkyung Jwa +2 more
doaj +1 more source
A Feature Fusion Based Indicator for Training-Free Neural Architecture Search
IEEE Access, 2021 Neural Architecture Search Without Training (NASWOT) has been proposed recently to replace the conventional Neural Architecture Search (NAS). Pioneer works only deploy one or two indicator(s) to search.
Linh-Tam Tran +2 more
doaj +1 more source
Channel Configuration for Neural Architecture: Insights from the Search Space [PDF]
, 2023 We consider search spaces associated with neural network channel configuration. Architectures and their accuracy are visualised using low-dimensional Euclidean embedding (LDEE). Optimisation dynamics are captured using local optima networks (LONs).
Thomson, Sarah L +3 more
core +1 more source
Neural Architecture Search for Spiking Neural Networks
, 2022 Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-sparsity activation. However, most prior SNN methods use ANN-like architectures (e.g., VGG-Net or ResNet), which could provide sub-optimal performance for temporal sequence ...
Youngeun Kim +4 more
openaire +4 more sources