Results 41 to 50 of about 6,198,210 (289)
RoCo-NAS: Robust and Compact Neural Architecture Search
, 2021 Deep model compression has been studied widely, and state-of-the-art methods can now achieve high compression ratios with minimum accuracy loss. Recent advances in adversarial attacks reveal the inherent vulnerability of deep neural networks to slightly ...
Mehdi Modarressi +8 more
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Revisiting Neural Architecture Search
CoRR, 2020 Neural Architecture Search (NAS) is a collection of methods to craft the way neural networks are built. Current NAS methods are far from ab initio and automatic, as they use manual backbone architectures or micro building blocks (cells), which have had minor breakthroughs in performance compared to random baselines.
Anubhav Garg +2 more
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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
doaj +1 more source
Towards Leveraging Structure for Neural Predictor in NAS [PDF]
Computer and Knowledge Engineering, 2022 Neural Architecture Search (NAS), which automatically designs a neural architecture for a specific task, has attracted much attention in recent years. Properly defining the search space is a key step in the success of NAS approaches, which allows us to ...
Saeedeh Eslami +2 more
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Neural architecture search as sparse supernet
, 2021 This paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-Path and Multi-Path Search to automated Mixed-Path Search.
WU, Y. +8 more
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Subarchitecture Ensemble Pruning in Neural Architecture Search [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2022 Accepted by TNNLS.
Yijun Bian +5 more
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Multi-Fidelity Neural Architecture Search With Knowledge Distillation
IEEE Access, 2023 Neural architecture search (NAS) targets at finding the optimal architecture of a neural network for a problem or a family of problems. Evaluations of neural architectures are very time-consuming. One of the possible ways to mitigate this issue is to use
Ilya Trofimov +4 more
doaj +1 more source
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
core
Pareto-optimal progressive neural architecture search [PDF]
, 2021 Neural Architecture Search (NAS) is the process of automating architecture engineering, searching for the best deep learning configuration. One of the main NAS approaches proposed in the literature, Progressive Neural Architecture Search (PNAS), seeks ...
Eugenio Lomurno +7 more
core +1 more source
Probabilistic Neural Architecture Search
CoRR, 2019 In neural architecture search (NAS), the space of neural network architectures is automatically explored to maximize predictive accuracy for a given task. Despite the success of recent approaches, most existing methods cannot be directly applied to large scale problems because of their prohibitive computational complexity or high memory usage.
Francesco Paolo Casale +2 more
openaire +3 more sources