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Understanding the wiring evolution in differentiable neural architecture search
CoRR, 2020 AISTATS ...
Sirui Xie +6 more
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Differentiable neural architecture search in equivalent space with exploration enhancement [PDF]
, 2023 Recent works on One-Shot Neural Architecture Search (NAS) mostly adopt a bilevel optimization scheme to alternatively optimize the supernet weights and architecture parameters after relaxing the discrete search space into a differentiable space. However,
Chang, X +5 more
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Applied Sciences, 2023 In the realm of machine learning, the optimization of hyperparameters and the design of neural architectures entail laborious and time-intensive endeavors.
Jiyong Shin, Kyongseok Park, Dae-Ki Kang
doaj +1 more source
Event Classification with Multi-step Machine Learning [PDF]
EPJ Web of Conferences, 2021 The usefulness and value of Multi-step Machine Learning (ML), where a task is organized into connected sub-tasks with known intermediate inference goals, as opposed to a single large model learned end-to-end without intermediate sub-tasks, is presented ...
Saito Masahiko +8 more
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IEEE Access, 2023 Mobile devices play a crucial role in human activity recognition as they enable real-time sensing of user interaction for learning algorithms like neural networks.
Won-Seon Lim +3 more
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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
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DE-DARTS: Neural architecture search with dynamic exploration
ICT Express, 2023 Neural architecture search (NAS) methods automatically find optimal neural networks without human assistance. Numerous algorithms for NAS have been studied to find architectures with gradient-based search.
Jiwoo Mun, Seokhyeon Ha, Jungwoo Lee
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A hybrid neural architecture search for hyperspectral image classification
Frontiers in Physics, 2023 Convolution neural network (CNN)is widely used in hyperspectral image (HSI) classification. However, the network architecture of CNNs is usually designed manually, which requires careful fine-tuning.
Aili Wang +4 more
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DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Training deep neural networks (DNNs) for meaningful differential privacy (DP) guarantees severely degrades model utility. In this paper, we demonstrate that the architecture of DNNs has a significant impact on model utility in the context of private deep learning, whereas its effect is largely unexplored in previous studies.
Anda Cheng +5 more
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iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients [PDF]
, 2021 Differentiable ARchiTecture Search(DARTS) has recently become the mainstream in the neural architecture search (NAS) due to its efficiency and simplicity.
Zhang, Miao +5 more
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