Journal ArticleDOI
Learning representations by back-propagating errors
Reads0
Chats0
TLDR
Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.Abstract:
We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal ‘hidden’ units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure1.read more
Citations
More filters
Journal ArticleDOI
Artificial neural networks for document analysis and recognition
TL;DR: This paper surveys the most significant problems in the area of offline document image processing, where connectionist-based approaches have been applied and depicts the most promising research guidelines in the field.
Journal ArticleDOI
Active Deep Learning for Classification of Hyperspectral Images
Peng Liu,Hui Zhang,Kie B. Eom +2 more
TL;DR: The proposed active learning algorithm based on a weighted incremental dictionary learning that trains a deep network efficiently by actively selecting training samples at each iteration is shown to be efficient and effective in classifying hyperspectral images.
Proceedings Article
Multimodal Residual Learning for Visual QA
Jin-Hwa Kim,Sang Woo Lee,Dong-Hyun Kwak,Min-Oh Heo,Jeonghee Kim,Jung-Woo Ha,Byoung-Tak Zhang +6 more
TL;DR: In this article, a multimodal residual network (MRN) was proposed to learn the joint representation from visual and language information, which achieved state-of-the-art results on the Visual QA dataset for both Open-Ended and Multiple-Choice tasks.
Posted Content
SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)
Marcos Zampieri,Preslav Nakov,Sara Rosenthal,Pepa Atanasova,Georgi Karadzhov,Hamdy Mubarak,Leon Derczynski,Zeses Pitenis,Çağrı Çöltekin +8 more
TL;DR: The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish.
Journal ArticleDOI
Potential, challenges and future directions for deep learning in prognostics and health management applications
TL;DR: A thorough evaluation of the current developments, drivers, challenges, potential solutions and future research needs in the field of deep learning applied to Prognostics and Health Management (PHM) applications can be found in this paper.