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Academic Articles & Research Papers
Showing 7 of 7 academic articles
Jiahui Yu, Linjie Yang, Ning Xu, Yang, Jianchao et al. • arXiv (Cornell University)
(2018)
We present a simple and general method to train a single neural network executable at different widths (number of channels in a layer), permitting instant and adaptive accuracy-efficiency trade-offs at runtime. Instead of training individua...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1812.08928
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter • arXiv (Cornell University)
(2017)
Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNN...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1706.02515
Neofytos Dimitriou, Ognjen Arandjelović • Information
(2022)
Normalization as a layer within neural networks has over the years demonstrated its effectiveness in neural network optimization across a wide range of different tasks, with one of the most successful approaches being that of batch normaliz...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/info13070337
Xia Zhao, Limin Wang, Yufei Zhang, Xuming Han et al. • Artificial Intelligence Review
(2024)
Abstract In computer vision, a series of exemplary advances have been made in several areas involving image classification, semantic segmentation, object detection, and image super-resolution reconstruction with the rapid development of dee...
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cc-by
DOI: https://doi.org/10.1007/s10462-024-10721-6
Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger • arXiv (Cornell University)
(2017)
Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike thos...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1706.04599
Shuaifeng Li, Heng Wang, Cong Zhang, Jie Liu • IEEE Access
(2022)
To address the problem that existing deep learning methods are not sufficiently accurate to detect rice pests with changeable shapes or similar appearances, a self-attention feature fusion model for rice pest detection (SAFFPest) was propos...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2022.3194925
Jianqing Fan, Cong Ma, Yiqiao Zhong • Statistical Science
(2021)
Deep learning has achieved tremendous success in recent years. In simple words, deep learning uses the composition of many nonlinear functions to model the complex dependency between input features and labels. While neural networks have a l...
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Open Access
DOI: https://doi.org/10.1214/20-sts783
PMC: 8300482