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NAS-FCOS: Fast Neural Architecture Search for Object Detection [PDF]

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020
9 pages, 9 figures, accepted by CVPR ...
Yanning Zhang, Ning Wang, Chunhua Shen
exaly   +5 more sources

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NASGuard: A Novel Accelerator Architecture for Robust Neural Architecture Search (NAS) Networks

2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA), 2021
Due to the wide deployment of deep learning applications in safety-critical systems, robust and secure execution of deep learning workloads is imperative. Adversarial examples, where the inputs are carefully designed to mislead the machine learning model is among the most challenging attacks to detect and defeat.
Xingbin Wang, Boyan Zhao, Rui Hou 0001, Amro Awad, Zhihong Tian, Dan Meng 0002   +5 more
openaire   +1 more source

NAS-PED: Neural Architecture Search for Pedestrian Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian detection currently suffers from two issues in crowded scenes: occlusion and dense boundary prediction, making it still challenging in complex real-world scenarios. In recent years, Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have shown their superiorities in addressing these issues, where ViTs capture global feature ...
Yi Tang, Min Liu 0008, Baopu Li, Yaonan Wang 0001, Wanli Ouyang   +4 more
openaire   +3 more sources

FTR-NAS: Fault-Tolerant Recurrent Neural Architecture Search

2020
With the popularity of the applications equipped with neural networks on edge devices, robustness has become the focus of researchers. However, when deploying the applications onto the hardware, environmental noise is unavoidable, in which errors may cause applications crash, especially for the safety-critic applications.
Kai Hu, Dong Ding, Shuo Tian, Rui Gong, Li Luo, Lei Wang 0011   +5 more
openaire   +2 more sources

Neural Architecture Search (NAS) for Vision Tasks

2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)
Nilesh Bhosle, Hassan M Al-Jawahry, Kamaljeet Kaur   +2 more
exaly   +2 more sources

Search-Efficient NAS: Neural Architecture Search for Classification

2022 19th International SoC Design Conference (ISOCC), 2022
Amrita Rana, Kyung Ki Kim
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Open-NAS: A customizable search space for Neural Architecture Search

Proceedings of the 2023 8th International Conference on Machine Learning Technologies, 2023
Leo Pouy, Fouad Khenfri, Patrick Leserf, Chokri Mraidha, Chérif Larouci   +4 more
openaire   +1 more source

CaW-NAS: Compression Aware Neural Architecture Search

2022 25th Euromicro Conference on Digital System Design (DSD), 2022
Hadjer Benmeziane, Hamza Ouarnoughi, Smaïl Niar, Kaoutar El Maghraoui   +3 more
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Bandit-NAS: Bandit Sampling Method for Neural Architecture Search

2023 International Joint Conference on Neural Networks (IJCNN), 2023
Yiqi Lin, Ru Wang
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Dag-Nas: An Explainable Neural Architecture Search Framework for Reinforcement Learning

Neural Networks
We present an explainable Neural Architecture Search (NAS) framework for Reinforcement Learning (RL). We model a feed-forward neural network as a Directed Acyclic Graph (DAG) that consists of scalar-level operations and their interconnections. Scalar-level DAGs are trained using a differentiable search method, followed by pruning search results.
Taegun An, Changhee Joo
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