Journal ArticleDOI
A comprehensive survey and analysis of generative models in machine learning
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TLDR
This paper review and analyse critically all the generative models, namely Gaussian Mixture Models (GMM), Hidden Markov Models (HMM), Latent Dirichlet Allocation (LDA), Restricted Boltzmann Machines (RBM), Deep Belief Networks (DBN), Deep Boltz Mann Machines (DBM), and GANs, to provide the reader some insights on which generative model to pick from while dealing with a problem.About:
This article is published in Computer Science Review.The article was published on 2020-11-01. It has received 117 citations till now. The article focuses on the topics: Generative model & Deep belief network.read more
Citations
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Journal ArticleDOI
Machine learning algorithms for social media analysis: A survey
TL;DR: A comprehensive survey of multiple applications of SM analysis using robust machine learning algorithms, which are used in SM analysis.
Journal ArticleDOI
Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects
Francesco Pacileo,Xin Lai,Yunfeng Huang,Huanghui Gu,Xuebing Han,Xuning Feng,Haifeng Dai,Yuejiu Zheng,Minggao Ouyang +8 more
TL;DR: An RDE estimation method based on the future load prediction considering battery temperature and ageing effects is proposed, and a battery simulation driving condition is constructed using the real vehicle speed to verify the effectiveness of the proposed method in complex conditions.
Journal ArticleDOI
Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects
TL;DR: In this article , an RDE estimation method based on the future load prediction considering battery temperature and ageing effects is proposed, in which the hidden Markov model (HMM) is implemented to predict the battery load and the capacity test at different temperatures is conducted to determine the limited state-of-charge (SOC) in the prediction field.
Proceedings ArticleDOI
A Dual Approach for Credit Card Fraud Detection using Neural Network and Data Mining Techniques
TL;DR: In this paper, the authors build models to detect fraudulent credit card transactions using five classifiers to find out the best fit classifier for the situation and use two different techniques to tackle the inherent problem of data imbalance.
Journal ArticleDOI
UBMTR: Unsupervised Boltzmann Machine-Based Time-Aware Recommendation System
TL;DR: An unsupervised Boltzmann machine-based time-aware recommendation system (UBMTR) which detects underlying hidden features in user-movie ratings data in connection with the time at which each rating was made (temporal information).
References
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Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Journal ArticleDOI
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Proceedings Article
Latent Dirichlet Allocation
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Proceedings Article
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.