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Academic Articles & Research Papers
Showing 20 of 547 academic articles
Berthold K. P. Horn (1986)
From the Publisher:
This book presents a coherent approach to the fast-moving field of computer vision, using a consistent notation based on a detailed understanding of the image formation process. It covers even the most recent research a...
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Raviraju Balappa D, Gautam Kumar Rajput • Shodh Sari
(2025)
Matrices are foundational to artificial intelligence (AI), serving as critical tools for data representation, manipulation, and transformation across various applications. From machine learning algorithms to neural network architectures, ma...
DOAJ
Open Access (DOAJ)
DOI: https://doi.org/10.59231/SARI7840
Fabien Charmet, Harry Chandra Tanuwidjaja, Solayman Ayoubi, Pierre-François Gimenez et al. • Annals of Telecommunications
(2022)
Abstract With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essent...
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DOI: https://doi.org/10.1007/s12243-022-00926-7
Peratham Wiriyathammabhum, Douglas A. Summers-Stay, Cornelia M. Fermuller, Yiannis Aloimonos • ACM Computing Surveys
(2016)
Integrating computer vision and natural language processing is a novel interdisciplinary field that has received a lot of attention recently. In this survey, we provide a comprehensive introduction of the integration of computer vision and ...
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DOI: https://doi.org/10.1145/3009906
Yang Liu, Meng Zhang • Computational Linguistics
(2017)
Deep learning has attracted dramatic attention in recent years, both in academia and industry. The popular term deep learning generally refers to neural network methods. Indeed, many core ideas and methods were born years ago in the era of ...
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cc-by-nc-nd
DOI: https://doi.org/10.1162/coli_r_00312
Jian Feng Guo, He He, Tong He, Leonard Lausen et al. • arXiv (Cornell University)
(2019)
We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training l...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1907.04433
Tatwadarshi P. Nagarhalli, Vinod Moreshwar Vaze, Nirav Pravinsinh Rana • International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)
(2021)
The recent times have seen unprecedented growth in research and development in the field of Artificial Intelligence and its allied fields, subfields, like Machine Learning, Deep Learning and Natural Language Processing. The new found excite...
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DOI: https://doi.org/10.1109/icicv50876.2021.9388380
Lourdes Agapito, Michael M. Bronstein, Carsten Rother • Lecture notes in computer science
(2015)
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DOI: https://doi.org/10.1007/978-3-319-16181-5
Binh‐Son Hua, Minh-Khoi Tran, Sai-Kit Yeung • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
(2018)
Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with convolutional neural network has been so far not fully explored....
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Open Access
DOI: https://doi.org/10.1109/cvpr.2018.00109
Saad Albawi, Tareq Abed Mohammed, Saad Al-Azawi (2017)
The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literatur...
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DOI: https://doi.org/10.1109/icengtechnol.2017.8308186
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner et al. • IEEE Transactions on Neural Networks
(2008)
Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper,...
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Open Access
DOI: https://doi.org/10.1109/tnn.2008.2005605
Abhay B. Bulsari • Elsevier eBooks
(1995)
Part 1 Theoretical issues: an introduction to artificial neural networks, D.T. Pham unsupervised neural learning, E. Oja back-propagation and its variations, D. Tsaptsinos the general approximation problem for feed-forward neural networks, ...
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Hamid R. Tizhoosh, Liron Pantanowitz • Journal of Pathology Informatics
(2018)
In light of the recent success of artificial intelligence (AI) in computer vision applications, many researchers and physicians expect that AI would be able to assist in many tasks in digital pathology. Although opportunities are both manif...
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cc-by-nc-sa
DOI: https://doi.org/10.4103/jpi.jpi_53_18
PMC: 6289004
Masoud Yazdani • Chapman & Hall eBooks
(1986)
Part 1. Principles of artificial intelligence, John Campbell. Part 2 Tools and techniques: artificial intelligence programming environments and the POPLOG system, John Gibson LISP, lists and pattern-matching, Tony Hasemer. Part 3 Applicatio...
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Souza VHS, Rahman M, Silva MCDS, Benicio LM et al. • Translational Animal Science
(2026)
PMC
PMC Public Access (View-Only)
DOI: 10.1093/tas/txag150
PMC: PMC13739279
Lucas Francisco Amaral Orosco Pellicer, Taynan Maier Ferreira, Anna Helena Reali Costa • Applied Soft Computing
(2022)
Data Augmentation (DA) methods – a family of techniques designed for synthetic generation of training data – have shown remarkable results in various Deep Learning and Machine Learning tasks. Despite its widespread and successful adoption w...
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cc-by-nc-nd
DOI: https://doi.org/10.1016/j.asoc.2022.109803
Stefano Ferilli • Advances in computer vision and pattern recognition
(2011)
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DOI: https://doi.org/10.1007/978-0-85729-198-1_6
Wei Emma Zhang, Quan Z. Sheng, Ahoud Alhazmi, Chenliang Li • ACM Transactions on Intelligent Systems and Technology
(2020)
With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs are vulne...
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Open Access
DOI: https://doi.org/10.1145/3374217
Amirsina Torfi, Rouzbeh A. Shirvani, Yaser Keneshloo, Nader Tavaf et al. • arXiv (Cornell University)
(2020)
Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication. Recent developments in computational power and the advent of l...
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Open Access
DOI: https://doi.org/10.48550/arxiv.2003.01200
Bohan Li, Yutai Hou, Wanxiang Che • AI Open
(2022)
As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail. It is widely applied in computer vision then introduced to natural language processing and achieves improvements in...
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DOI: https://doi.org/10.1016/j.aiopen.2022.03.001