Fetching the paper…
Reading the bibliography…
Mobility datasets are fundamental for evaluating algorithms pertaining to geographic information systems and facilitating experimental reproducibility.
Fonctions de répartition à n dimensions et leurs marges
Abe Sklar · 1959
Earlier work this paper cites.
Convolutional networks for images, speech, and time series
Yann LeCun, Yoshua Bengio, et al · 1995
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
On the generation of spatiotemporal datasets
Yannis Theodoridis, Jefferson RO Silva, and Mario A Nascimento · 1999
Earlier work this paper cites.
A framework for generating network-based moving objects
Thomas Brinkhoff · 2002
Earlier work this paper cites.
Synthetic and real spatiotemporal datasets
Mario A. Nascimento, Dieter Pfoser, and Yannis Theodoridis · 2003
Earlier work this paper cites.
Synthetic generation of cellular network positioning data
Fosca Giannotti, Andrea Mazzoni, Simone Puntoni, and Chiara Renso · 2005
Earlier work this paper cites.
St–acts: a spatio-temporal activity simulator
Gyozo Gidofalvi and Torben Bach Pedersen · 2006
Earlier work this paper cites.
A utility-theoretic approach to privacy and personalization
Andreas Krause and Eric Horvitz · 2008
Earlier work this paper cites.
Sole: scalable on-line execution of continuous queries on spatio-temporal data streams
Mohamed F Mokbel and Walid G Aref · 2008
Earlier work this paper cites.
Berlinmod: a benchmark for moving object databases
Christian Düntgen, Thomas Behr, and Ralf Hartmut Güting · 2009
Earlier work this paper cites.
Towards rich mobile phone datasets: Lausanne data collection campaign
Niko Kiukkonen, Jan Blom, Olivier Dousse, Daniel Gatica-Perez, and Juha Laurila · 2010
Earlier work this paper cites.
Geolife: A collaborative social networking service among user, location and trajectory
Yu Zheng, Xing Xie, and Wei-Ying Ma · 2010
Earlier work this paper cites.
Sumo–simulation of urban mobility
Michael Behrisch, Laura Bieker, Jakob Erdmann, and Daniel Krajzewicz · 2011
Earlier work this paper cites.
Quantifying location privacy
Reza Shokri, George Theodorakopoulos, Jean-Yves Le Boudec, and Jean-Pierre Hubaux · 2011
Cited alongside, same era.
A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
Cited alongside, same era.
The mobile data challenge: Big data for mobile computing research
Juha K Laurila, Daniel Gatica-Perez, Imad Aad, Olivier Bornet, Trinh-Minh-Tri Do, Olivier Dousse, Julien Eberle, Markus Miettinen, et al · 2012
Cited alongside, same era.
Mwgen: a mini world generator
Jianqiu Xu and Ralf Hartmut Güting · 2012
Cited alongside, same era.
Recurrent neural networks
Stephen Grossberg · 2013
Cited alongside, same era.
A hierarchical hidden semi-markov model for modeling mobility data
Mitra Baratchi, Nirvana Meratnia, Paul JM Havinga, Andrew K Skidmore, and Bert AKG Toxopeus · 2014
Cited alongside, same era.
Real-valued (medical) time series generation with recurrent conditional gans
Cristóbal Esteban, Stephanie L Hyland, and Gunnar Rätsch · 2017
Later among the works it cites.
Probit transformation for nonparametric kernel estimation of the copula density
Gery Geenens, Arthur Charpentier, and Davy Paindaveine · 2017
Later among the works it cites.
Generating synthetic mobility traffic using rnns
Vaibhav Kulkarni and Benoît Garbinato · 2017
Later among the works it cites.
Priva’mov: Analysing human mobility through multi-sensor datasets
Sonia Ben Mokhtar, Antoine Boutet, Louafi Bouzouina, Patrick Bonnel, Olivier Brette, Lionel Brunie, Mathieu Cunche, Stephane D’Alu, Vincent Primault, Patrice Raveneau, et al · 2017
Later among the works it cites.
Knock knock, who’s there? membership inference on aggregate location data
Apostolos Pyrgelis, Carmela Troncoso, and Emiliano De Cristofaro · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dependence Modeling with Copulas
Harry Joe · 2014
Cited alongside, same era.
Hermoupolis: a semantic trajectory generator in the data science era
Nikos Pelekis, Stylianos Sideridis, Panagiotis Tampakis, and Yannis Theodoridis · 2015
Cited alongside, same era.
Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2015
Cited alongside, same era.
Non-markovian character in human mobility: Online and offline
Zhi-Dan Zhao, Shi-Min Cai, and Yang Lu · 2015
Cited alongside, same era.
Synthesizing plausible privacy-preserving location traces
Vincent Bindschaedler and Reza Shokri · 2016
Cited alongside, same era.
Critical behavior from deep dynamics: a hidden dimension in natural language
Henry W Lin and Max Tegmark · 2016
Cited alongside, same era.
Trajectory recovery from ash: User privacy is not preserved in aggregated mobility data
Fengli Xu, Zhen Tu, Yong Li, Pengyu Zhang, Xiaoming Fu, and Depeng Jin · 2017
Later among the works it cites.
Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
Later among the works it cites.
Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, and Jürgen Schmidhuber · 2017
Later among the works it cites.
On the inability of markov models to capture criticality in human mobility
Vaibhav Kulkarni, Abhijit Mahalunkar, Benoit Garbinato, and John D Kelleher · 2018
Closest in time.
Abhijit Mahalunkar and John D Kelleher · 2018
Closest in time.
rvinecopulib: high performance algorithms for vine copula modeling, 2018
Thomas Nagler and Thibault Vatter · 2018
Closest in time.
Machine learning with membership privacy using adversarial regularization
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
Closest in time.
A non-parametric generative model for human trajectories
Kun Ouyang, Reza Shokri, David S Rosenblum, and Wenzhuo Yang · 2018
Closest in time.