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Gaussian Processes for Natural Language Processing

Trevor Cohn, Daniel Preotiuc-Pietro, Neil Lawrence
2014 Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: Tutorials  
In the final section of the tutorial we give a brief overview of advanced topics in the field of GPs. First, we look at non-conjugate likelihoods for modelling classification, count and rank data.  ...  He has been programme chair for top machine learning conferences (NIPS, AISTATS), and has run several past tutorials on Gaussian Processes. ) Non-congjugate likelihoods: classification, counts and ranking  ... 
doi:10.3115/v1/p14-6001 dblp:conf/acl/CohnPL14 fatcat:plnqnt2t4rhbjlbg2oj6zxlgw4

A Perspective on Gaussian Processes for Earth Observation

Gustau Camps-Valls, Dino Sejdinovic, Jakob Runge, Markus Reichstein
2019 National Science Review  
In the last decade, machine learning and Gaussian processes (GPs) in particular has attained outstanding results in the estimation of bio-geo-physical variables from the acquired images at local and global  ...  Despite great advances in forward and inverse modelling, GP models still have to face important challenges that are revised in this perspective paper.  ...  Gaussian processes (GPs) [1] , as flexible non-parametric models to find functional relationships, have excelled in EO problems in recent years, mainly introduced for model inversion and emulation of  ... 
doi:10.1093/nsr/nwz028 pmid:34691913 pmcid:PMC8291600 fatcat:o2hsdgspajb6hgak572nhb4ree

Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics

Ryan P. Adams, Emily B. Fox, Erik B. Sudderth, Yee Whye Teh
2015 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Due to recent computational advances, these approaches have received increasing attention in machine learning, statistics, probability, and related application domains.  ...  For Gaussian process models of regression and classification functions, the parameter space consists of a set of continuous functions.  ...  Due to recent computational advances, these approaches have received increasing attention in machine learning, statistics, probability, and related application domains.  ... 
doi:10.1109/tpami.2014.2380478 pmid:26598765 fatcat:cuulndonrff4ffvneirnnzariy

Comparison of Machine Learning Algorithms for Induction MotorRotor SingleFault Diagnosis using Stator Current Signal

2021 International Journal of Advanced Trends in Computer Science and Engineering  
This quantitive researchwas conducted to compare the efficiency between three groups of machine learning' classification techniques for detecting broken rotor bar (BRB)fault in induction motor using stator  ...  Thus, the main purpose of the article is to find out the most suitable method of distributing and extracting data for the fault diagnosis problems.  ...  ACKNOWLEDGEMENTS This work was supported in part by ShanghaiPujiang Program, and in part by Belt andRoad InternationalCooperation Project.  ... 
doi:10.30534/ijatcse/2021/031032021 fatcat:5wej3ikonrhkrartzf7k4bu4ci

Astro2020 Science White Paper: The Next Decade of Astroinformatics and Astrostatistics [article]

A.Siemiginowska, G. Eadie, I. Czekala, E. Feigelson, E.B. Ford, V. Kashyap, M. Kuhn, T.Loredo, M.Ntampaka, A. Stevens, A. Avelino, K. Borne (+37 others)
2019 arXiv   pre-print
New methodologies derived from advances in statistics, computer science, and machine learning are beginning to be employed in sophisticated investigations that are not only bringing forth new discoveries  ...  Over the past century, major advances in astronomy and astrophysics have been largely driven by improvements in instrumentation and data collection.  ...  models, computational power; Gaussian processes in time domain for Poisson (Cox process) X-ray and gamma-ray [83, 82, 81, 130] ; use of higher order Fourier product and non-linear signal processing  ... 
arXiv:1903.06796v1 fatcat:odd6ph2xjfdproha4zxdd2dwii

Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey [article]

Kai Chen, Qinglei Kong, Yijue Dai, Yue Xu, Feng Yin, Lexi Xu, Shuguang Cui
2021 arXiv   pre-print
In this paper, we review a promising family of nonparametric Bayesian machine learning methods, i.e., Gaussian processes (GPs), and their applications in wireless communication.  ...  Empowered by big data and machine learning, next-generation data-driven communication systems will be intelligent with the characteristics of expressiveness, scalability, interpretability, and especially  ...  To promote interpretable machine learning for data-driven wireless communications, in this paper, we review the Gaussian process (GP) model, and present their applications in wireless communications due  ... 
arXiv:2103.10134v3 fatcat:bhox7nbavvcb7lnzndu2zr44r4

Parametric Versus Non-Parametric Time Series Forecasting Methods: A Review

Anjali Gautam, Department of Information Technology, Indian Institute of Information Technology, Deoghat Jhalwa, Allahabad-211015, Uttar Pradesh, India, Vrijendra Singh
2020 Journal of Engineering Science and Technology Review  
Additionally, the limitations of the machine learning methods are highlighted which leads to the selection of parametric methods over non-parametric methods by the researchers in recent years.  ...  The non-parametric methods have been proposed in the research literature as an alternative to parametric methods for time series forecasting.  ...  Reasons for applicability of parametric methods in machine learning era In recent years, non-parametric models gained popularity in the field of time series forecasting due to its ability to capture subtle  ... 
doi:10.25103/jestr.133.18 fatcat:edvagysdprg67cv3qh7pzvhr5q

A computational mechanics special issue on: data-driven modeling and simulation—theory, methods, and applications

Wing Kam Liu, George Karniakis, Shaoqiang Tang, Julien Yvonnet
2019 Computational Mechanics  
With such explosive growth of available data and computing resources, recent advances in machine learning and data analytics have yielded transformative results across diverse scientific disciplines, including  ...  Maziar Raissi, et al., introduced parametric Gaussian process regression for big data.  ... 
doi:10.1007/s00466-019-01741-z fatcat:aon3rff43bgdbfjmow7r2csmza

Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders [article]

Hongyu Shen, Daniel George, E. A. Huerta, Zhizhen Zhao
2017 arXiv   pre-print
To overcome these issues, we apply state-of-the-art signal processing techniques, based on recent groundbreaking advancements in deep learning, to denoise gravitational wave signals embedded either in  ...  wave signals embedded in real non-Gaussian LIGO noise.  ...  INTRODUCTION The application of machine learning and deep learning techniques have recently driven disruptive advances across many domains in engineering, science, and technology [1] .  ... 
arXiv:1711.09919v1 fatcat:6gliympsyjfmracb7b6ojmsjx4

Advanced Driver-Assistance System with Traffic Sign Recognition for Safe and Efficient Driving

Sithmini Gunasekara, Dilshan Gunarathna, Maheshi Dissanayake
2021 International Journal on Recent and Innovation Trends in Computing and Communication  
In the proposed method, same priority was given to processing time (testing time) and accuracy, as traffic sign identification is time critical.  ...  The final accuracy obtained was 87% (with confidence interval 84%-90%) with a processing time of 0.64s (with confidence interval of 0.57s-0.67s) for correct detection at testing, which emphasizes the effectiveness  ...  In the study presented, we are proposing a traditional machine learning algorithm with Support Vector Machines (SVM) for traffic sign recognition.  ... 
doi:10.17762/ijritcc.v9i9.5488 fatcat:pzbli5ptm5d57h5mykb45shq2e

Keystroke Dynamics User Authentication Using Advanced Machine Learning Methods [chapter]

Yunbin Deng, Yu Zhong
2015 Gate to Computer Science and Research  
2) identity vector (i-vector) approach to user modelling, and 3) deep machine learning approach.  ...  In this chapter, we adopt three popular voice biometrics algorithms to perform keystroke dynamics based user authentication, namely, 1) Gaussian Mixture Model with Universal Background Model (GMM-UBM),  ...  We report performance of recent advanced algorithms on this data set in Section 2.4.  ... 
doi:10.15579/gcsr.vol2.ch2 fatcat:p77fysjrl5fvvjfjsrwquuma24

Deep Bayesian Active Learning, A Brief Survey on Recent Advances [article]

Salman Mohamadi, Hamidreza Amindavar
2022 arXiv   pre-print
In this paper, we briefly survey recent advances in Bayesian active learning and in particular deep Bayesian active learning frameworks.  ...  On the other hand, from the real world application perspective, uncertainty representation is getting more and more attention in the machine learning community.  ...  However recent advances in machine learning and in particular, artificial neural network, have shown that non-parametric models, are capable of almost modeling any type of data at the cost of higher complexity  ... 
arXiv:2012.08044v2 fatcat:g4oyahxjerg6vgomhg5jyutwbu

Machine Learning in Chemical Engineering : A Perspective

Artur M. Schweidtmann, Erik Esche, Asja Fischer, Marius Kloft, Jens-Uwe Repke, Sebastian Sager, Alexander Mitsos
2021 Chemie - Ingenieur - Technik (2021). doi:10.1002/cite.202100083  
Recent breakthroughs in machine learning (ML) provide unique opportunities, but only joint interdisciplinary research between the ML and chemical engineering (CE) communities will unfold the full potential  ...  representation, (4) heterogeneity of data, (5) safety and trust in ML applications, and (6) creativity.  ...  The authors gratefully acknowledge the DFG for establishing the Priority Programme SPP 2331 ''Machine learning in chemical engineering.  ... 
doi:10.18154/rwth-2021-09826 fatcat:7tlvcx22urd27fpjfbw4iqhr7a

New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)

Arthur Gretton, Philipp Hennig, Carl Edward Rasmussen, Bernhard Schölkopf, Marc Herbstritt
2017 Dagstuhl Reports  
The Dagstuhl Seminar on 16481 "New Directions for Learning with Kernels and Gaussian Processes" brought together two principal theoretical camps of the machine learning community at a crucial time for  ...  Kernel methods and Gaussian process models together form a significant part of the discipline's foundations, but their prominence is waning while more elaborate but poorly understood hierarchical models  ...  In this talk, I briefly overviewed recent developments in the field of automated machine learning, which gives rise to very popular applications of Gaussian processes in Bayesian optimization.  ... 
doi:10.4230/dagrep.6.11.142 dblp:journals/dagstuhl-reports/GrettonHRS16 fatcat:sky4bixr6fg3djtboe6ueirthi

Machine learning in remote sensing data processing

Gustavo Camps-Valls
2009 2009 IEEE International Workshop on Machine Learning for Signal Processing  
This paper serves as a survey of methods and applications, and reviews the latest methodological advances in machine learning for remote sensing data analysis.  ...  To treat efficiently the acquired data and provide accurate products, remote sensing has evolved into a multidisciplinary field, where machine learning and signal processing algorithms play an important  ...  vector machines (RVM) [56] , or Gaussian Processes (GP) [57] .  ... 
doi:10.1109/mlsp.2009.5306233 fatcat:tb3on4evwvdvpkri67dbph7zfy
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