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
NetKet: A machine learning toolkit for many-body quantum systems
Giuseppe Carleo,Kenny Choo,Damian Hofmann,James E. T. Smith,Tom Westerhout,Fabien Alet,Emily Davis,Stavros Efthymiou,Ivan Glasser,Sheng-Hsuan Lin,Marta Mauri,Guglielmo Mazzola,Christian B. Mendl,Evert van Nieuwenburg,Ossian O'Reilly,Hugo Théveniaut,Giacomo Torlai,Filippo Vicentini,Alexander Wietek +18 more
TL;DR: NetKet provides algorithms for several key tasks in quantum many-body physics and quantum technology, namely quantum state tomography, supervised learning from wave-function data, and ground state searches for a wide range of customizable lattice models.
Journal ArticleDOI
Ensemble of convolutional neural networks for bioimage classification
TL;DR: This work presents a system based on an ensemble of Convolutional Neural Networks and descriptors for bioimage classification that has been validated on different datasets of color images and obtains state-of-the-art performance across four different bioimage and medical datasets.
Journal ArticleDOI
Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern
Jiaqi Qu,Zheng Qian,Yan Pei +2 more
TL;DR: An attention-based long-term and short-term temporal neural network prediction model (ALSM) assembled using the convolutional neural network (CNN), longShort-term memory neural network(LSTM), and attention mechanism under the multiple relevant and target variables prediction pattern (MRTPP) demonstrates more superiority compared to a few PV power forecasting methods.
Book ChapterDOI
Shuffle-Then-Assemble: Learning Object-Agnostic Visual Relationship Features
TL;DR: Extensive experiments on two visual relationship benchmarks show that by using the novel Shuffle-Then-Assemble pre-trained features, naive relationship models can be consistently improved and even outperform other state-of-the-art relationship models.
Journal ArticleDOI
Multi-Scale Deep Residual Learning-Based Single Image Haze Removal via Image Decomposition
TL;DR: A novel deep learning-based architecture for single image haze removal relying on multi-scale residual learning (MSRL) and image decomposition and Experimental results have demonstrated good effectiveness of the proposed framework, compared with state-of-the-art approaches.
References
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Proceedings Article
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Gradient-based learning applied to document recognition
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