Results 41 to 50 of about 75,461 (296)

DistilNAS: Neural Architecture Search With Distilled Data

IEEE Access, 2022
Can we perform Neural Architecture Search (NAS) with a smaller subset of target dataset and still fair better in terms of performance with significant reduction in search cost?
Swaroop N. Prabhakar, Ankur Deshwal, Rahul Mishra, Hyeonsu Kim   +3 more
doaj   +1 more source

Differentiable neural architecture search in equivalent space with exploration enhancement [PDF]

, 2020
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, Zhang, M, Li, H, Su, S, Pan, S, Ge, Z   +5 more
core   +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, Reza Monsefi, Mohammad Akbari   +2 more
doaj   +1 more source

Subarchitecture Ensemble Pruning in Neural Architecture Search [PDF]

IEEE Transactions on Neural Networks and Learning Systems, 2022
Accepted by TNNLS.
Yijun Bian, Qingquan Song, Mengnan Du, Jun Yao, Huanhuan Chen 0001, Xia Hu 0001   +5 more
openaire   +3 more sources

Differentiable Neural Architecture, Mixed Precision and Accelerator Co-Search

IEEE Access, 2023
Quantization, effective Neural Network architecture, and efficient accelerator hardware are three important design paradigms to maximize accuracy and efficiency.
Krishna Teja Chitty-Venkata, Yiming Bian, Murali Emani, Venkatram Vishwanath, Arun K. Somani   +4 more
doaj   +1 more source

One-Shot Neural architecture search via novelty driven sampling [PDF]

, 2020
One-Shot Neural architecture search (NAS) has received wide attentions due to its computational efficiency. Most state-of-the-art One-Shot NAS methods use the validation accuracy based on inheriting weights from the supernet as the stepping stone to ...
Zhang, Miao, Taoping Liu, Shirui Pan, Li, Huiqi, Su, Steven, Pan, Shirui, Miao Zhang, Huiqi Li, Steven Su, Liu, Taoping   +9 more
core   +1 more source

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, Nikita Klyuchnikov, Mikhail Salnikov, Alexander Filippov, Evgeny Burnaev   +4 more
doaj   +1 more source

Topology-Sensitive Neural Architecture Search for Language Modeling

IEEE Access, 2021
Recently Neural Architecture Search has drawn interest from researchers because of its ability to learn neural network architectures from data automatically.
Quan Du, Nuo Xu, Yinqiao Li, Tong Xiao, Jingbo Zhu   +4 more
doaj   +1 more source

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

POPNASv2 : efficient neural architecture search through time-accuracy optimization [PDF]

, 2022
LAUREA MAGISTRALEAutomatizzare la ricerca di architetture di reti neurali efficaci per un determinato dataset è diventato un ambito estremamente rilevante negli ultimi anni. La tecnica più efficace in questo contesto è la "Neural Architecture Search"
FALANTI, ANDREA
core