Filters








5,240 Hits in 9.7 sec

Deep Regression Bayesian Network and Its Applications [article]

Siqi Nie, Meng Zheng, Qiang Ji
2017 arXiv   pre-print
In this paper, we review different structures of deep directed generative models and the learning and inference algorithms associated with the structures.  ...  We focus on a specific structure that consists of layers of Bayesian Networks due to the property of capturing inherent and rich dependencies among latent variables.  ...  INTRODUCTION Deep learning has become a major enabling technology for computer vision.  ... 
arXiv:1710.04809v1 fatcat:bwlvhlwdbndnvjjvfdsw4mfnz4

A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges [article]

Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
2021 arXiv   pre-print
Bayesian approximation and ensemble learning techniques are two most widely-used UQ methods in the literature.  ...  This study reviews recent advances in UQ methods used in deep learning. Moreover, we also investigate the application of these methods in reinforcement learning (RL).  ...  Computer vision and image processing: As discussed earlier, computer vision and image processing are two main research domains for the application of different UQ methods.  ... 
arXiv:2011.06225v4 fatcat:wwnl7duqwbcqbavat225jkns5u

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
2021 Information Fusion  
This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlights fundamental research challenges and directions  ...  In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., selfdriving cars and object detection), image processing  ...  Acknowledgment This work was partially supported by the Australian Research Council's Discovery Projects funding scheme (project DP190102181) and the Natural Sciences and Engineering Research Council of  ... 
doi:10.1016/j.inffus.2021.05.008 fatcat:yschhguyxbfntftj6jv4dgywxm

Julia Language in Machine Learning: Algorithms, Applications, and Open Issues [article]

Kaifeng Gao, Jingzhi Tu, Zenan Huo, Gang Mei, Francesco Piccialli, Salvatore Cuomo
2020 arXiv   pre-print
The Julia language is a fast, easy-to-use, and open-source programming language that was originally designed for high-performance computing, which can well balance the efficiency and simplicity.  ...  Then, it investigates applications of the machine learning algorithms implemented with the Julia language.  ...  Acknowledgments This research was jointly supported by the National Natural Science Founda-  ... 
arXiv:2003.10146v1 fatcat:f2ocidpu4rchnokkc46qzrjgyu

Julia language in machine learning: Algorithms, applications, and open issues

Kaifeng Gao, Gang Mei, Francesco Piccialli, Salvatore Cuomo, Jingzhi Tu, Zenan Huo
2020 Computer Science Review  
The Julia language is a fast, easy-to-use, and open-source programming language that was originally designed for high-performance computing, which can well balance the efficiency and simplicity.  ...  Then, it investigates applications of the machine learning algorithms implemented with the Julia language.  ...  The authors would like to thank the editor and the reviewers for their valuable comments.  ... 
doi:10.1016/j.cosrev.2020.100254 fatcat:gdt66djfvjfqpjou3lvemxsxfy

Uncertainty-aware deep learning for robot touch: Application to Bayesian tactile servo control [article]

Manuel Floriano Vazquez, Nathan F. Lepora
2021 arXiv   pre-print
This work investigates uncertainty-aware deep learning (DL) in tactile robotics based on a general framework introduced recently for robot vision.  ...  deterministic networks.  ...  The use of probabilistic architectures in deep learning has the potential to bring many advantages when integrated in robotic applications.  ... 
arXiv:2104.14184v1 fatcat:quyqdwlwqfcitmbksdzmhzsqaq

MACHINE LEARNING IN AGRICULTURE: TECHNIQUES AND APPLICATIONS

Neha Yadav, Sk Md Alfayeed, Ankita Wadhawan
2020 International Journal of Engineering Applied Sciences and Technology  
For grouping, regression, and clustering, SVMs were used.  ...  Bayesian Belief Network SVM constructs a linear, separating hyperplane for classifying data instances, intrinsically a binary classifier.  ...  Bayesian Models The Bayesian models are the probabilistic class of graphic models in which an experiment is carried out within the Bayesian reference sense.  ... 
doi:10.33564/ijeast.2020.v05i07.018 fatcat:hxxo5orau5bqvkpm6pn33whque

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

Muhammad Usama, Junaid Qadir, Aunn Raza, Hunain Arif, Kok-lim Alvin Yau, Yehia Elkhatib, Amir Hussain, Ala Al-Fuqaha
2019 IEEE Access  
INDEX TERMS Machine learning, deep learning, unsupervised learning, computer networks.  ...  The growing interest in applying unsupervised learning techniques in networking stems from their great success in other fields, such as computer vision, natural language processing, speech recognition,  ...  Transfer learning has been successfully applied in computer vision and NLP applications but its implementation for networking has not been witnessed-even though in principle, this can be useful in networking  ... 
doi:10.1109/access.2019.2916648 fatcat:xutxh3neynh4bgcsmugxsclkna

Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works

Ayush Singhal, Pradeep Sinha, Rakesh Pant
2017 International Journal of Computer Applications  
The review also discusses the contribution of deep learning integrated recommendation systems into several application domains.  ...  The review concludes by discussion of the impact of deep learning in recommendation system in various domain and whether deep learning has shown any significant improvement over the conventional systems  ...  Given the recent advances in the field of deep learning in various application domains such as computer vision and speech recognition, deep learning has been extended to the area of information retrieval  ... 
doi:10.5120/ijca2017916055 fatcat:m6icpquumbgczhrdnya7x35of4

Calibration and Uncertainty Quantification of Bayesian Convolutional Neural Networks for Geophysical Applications [article]

Lukas Mosser, Ehsan Zabihi Naeini
2021 arXiv   pre-print
Deep neural networks offer numerous potential applications across geoscience, for example, one could argue that they are the state-of-the-art method for predicting faults in seismic datasets.  ...  It has been shown that popular Deep Learning-based models are often miscalibrated, and due to their deterministic nature, provide no means to interpret the uncertainty of their predictions.  ...  We further would like to thank Geoscience Australia and ConocoPhillips Ltd. for providing access to the Canning dataset.  ... 
arXiv:2105.12115v1 fatcat:5ah5twtcyjc7fbm2xil7q4ln3q

A Trend Analysis of Machine Learning Research with Topic Models and Mann-Kendall Test

Deepak Sharma, Bijendra Kumar, Satish Chand
2019 International Journal of Intelligent Systems and Applications  
This paper aims to systematically examine the literature of machine learning for the period of 1968~2017 to identify and analyze the research trends.  ...  It is used to highlight the evolution regarding the previous and recent trends in research topics in the area of machine learning.  ...  prior probabilist 4.b Deep learning and artificial neural network artifici contrast convolut diverg multilay neuron perceptron propag recur restrict 4.c Computer vision and image analysis analysi  ... 
doi:10.5815/ijisa.2019.02.08 fatcat:36tnwwjx2bhhpdbkwsanpsudtq

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges [article]

Muhammad Usama, Junaid Qadir, Aunn Raza, Hunain Arif, Kok-Lim Alvin Yau, Yehia Elkhatib, Amir Hussain, Ala Al-Fuqaha
2017 arXiv   pre-print
We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking.  ...  The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal  ...  RBM is used for dimensionality reduction, clustering and feature learning in computer networks. ing intelligent systems in computer vision and natural language processing whereas their application in computer  ... 
arXiv:1709.06599v1 fatcat:llcg6gxgpjahha6bkhsitglrsm

A Deep Learning Framework for Prediction of the Mechanism of Action

Jingyuan Dai, Shahram Latifi
2021 International Journal of Computer Applications  
This paper puts forward a deep learning framework, MoA Net, which ensembles one residual network and five convolutional neural networks to predict MoA targets.  ...  To find optimal parameter sets, the authors implements Bayesian tuning techniques on each sub network of MoA Net. The study uses logarithmic loss function to evaluate the model's performance.  ...  The authors want to thank Meagan Madariaga-Hopkins for proof-reading this work.  ... 
doi:10.5120/ijca2021921383 fatcat:vsf65dw6avg4jicy2474ejntje

Deep Learning: Methods and Applications

Li Deng
2014 Foundations and Trends® in Signal Processing  
Selected Applications in Object Recognition and Computer Vision Over the past two years or so, tremendous progress has been made in applying deep learning techniques to computer vision, especially in the  ...  In Section 10, we cover selected applications of deep learning to image object recognition in computer vision.  ... 
doi:10.1561/2000000039 fatcat:vucffxhse5gfhgvt5zphgshjy4

An Efficient Scheme of Deep Convolution Neural Network for Multi View Face Detection

Shivkaran Ravidas, M.A. Ansari
2019 International Journal of Intelligent Systems and Applications  
Index Terms-Face detection, multi view face detection, deep learning, convolutional neural network (CNN) and Computer vision.  ...  Further, a probabilistic calculation of resemblance among the images of face will be conducted on the basis of the Bayesian analysis for achieving detection of various faces.  ...  Even though neural networks are adapted to tasks of computer vision for obtaining good performance for generalization, it is good to add before knowledge into architecture of the network.  ... 
doi:10.5815/ijisa.2019.03.06 fatcat:ojh6l5dxp5aqxpjwkmg2zp5g2m
« Previous Showing results 1 — 15 out of 5,240 results