Recent advances in convolutional neural networks
Jiuxiang Gu,Zhenhua Wang,Jason Kuen,Lianyang Ma,Amir Shahroudy,Bing Shuai,Ting Liu,Xingxing Wang,Gang Wang,Jianfei Cai,Tsuhan Chen +10 more
TLDR
A broad survey of the recent advances in convolutional neural networks can be found in this article, where the authors discuss the improvements of CNN on different aspects, namely, layer design, activation function, loss function, regularization, optimization and fast computation.About:
This article is published in Pattern Recognition.The article was published on 2018-05-01 and is currently open access. It has received 3125 citations till now. The article focuses on the topics: Deep learning & Convolutional neural network.read more
Citations
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Journal ArticleDOI
Deep-Learning-Based Precipitation Observation Quality Control
TL;DR: This automated QC approach uses Convolutional Neural Networks to classify bad observation values, incorporating a multi-classifier ensemble to achieve better QC performance and is an option for eliminating bad observations for various applications, including the pre-processing of training datasets for machine learning.
Proceedings ArticleDOI
Optimizing Relevance Maps of Vision Transformers Improves Robustness
Hila Chefer,I Schwartz,L. Wof +2 more
TL;DR: It is proposed to monitor the model’s relevancy signal and manipulate it such that the model is focused on the foreground object, resulting in a marked improvement in robustness to domain shifts is observed.
Proceedings ArticleDOI
Performance Analysis of Machine Learning and Deep Learning Models for Text Classification
C. M. Suneera,Jay Prakash +1 more
TL;DR: In this paper, the performance of different machine learning and deep learning algorithms for text classification was evaluated on the 20 newsgroups dataset and the results indicate that Logistic regression outperforms over other ML algorithms and a Bi-channel Convolution Neural Network model gains exciting results compared to other deep learning models.
Journal ArticleDOI
IDMIL: an alignment-free Interpretable Deep Multiple Instance Learning (MIL) for predicting disease from whole-metagenomic data
TL;DR: The proposed alignment-free approach provides higher accuracy in prediction by harnessing the capability of deep convolutional neural network (CNN) within a MIL framework and provides interpretability via neural attention mechanism, which allows for the identification of groups of sequences that are likely to be correlated to diseases providing the much-needed interpretation.
Journal ArticleDOI
Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch
Zefeng Xu,Baoshan Tang,XiangYu Zhang,Jin Feng Leong,Jieming Pan,Sonu Hooda,E. G. Zamburg,A. Thean +7 more
TL;DR: In this article , an efficient in-situ nonlinear accelerator comprising a unique solution-processed two-dimensional (2D) MoS2 Opto-Resistive RAM Switch (ORS), which exhibits tunable nonlinear resistance switching that allow to introduce nonlinearity to the photonic neuron which overcomes the linear voltage-power relationship of typical photonic components.
References
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