Fetching the paper…
Reading the bibliography…
Real-world data typically contain a large number of features that are often heterogeneous in nature, relevance, and also units of measure.
The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2001
Earlier work this paper cites.
An introduction to copulas
Roger B. Nelsen · 2006
Earlier work this paper cites.
Iterative non-linear dimensionality reduction with manifold sculpting
Michael Gashler, Dan Ventura, and Tony Martinez · 2007
Earlier work this paper cites.
Generalized neural-network representation of high-dimensional potential-energy surfaces
Jörg Behler and Michele Parrinello · 2007
Earlier work this paper cites.
Visualizing Data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
An information-theoretic approach to statistical dependence: Copula information
R. S. Calsaverini and R. Vicente · 2009
Earlier work this paper cites.
Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
Albert P Bartók, Mike C Payne, Risi Kondor, and Gábor Csányi · 2010
Earlier work this paper cites.
Atom-centered symmetry functions for constructing high-dimensional neural network potentials
J Behler · 2011
Earlier work this paper cites.
Representation Learning: A Review and New Perspectives
Y Bengio, A Courville, and P Vincent · 2013
Earlier work this paper cites.
Metric Learning: A Survey
Brian Kulis · 2013
Earlier work this paper cites.
On representing chemical environments
A P Bartók, Risi Kondor, and Gábor Csányi · 2013
Earlier work this paper cites.
Uk biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age
Cathie Sudlow, John Gallacher, Naomi Allen, Valerie Beral, Paul Burton, John Danesh, Paul Downey, Paul Elliott, Jane Green, Martin Landray, et al · 2015
Earlier work this paper cites.
A review of feature selection methods with applications
A. Jović, K. Brkić, and N. Bogunović · 2015
Earlier work this paper cites.
Facial expression recognition with convolutional neural networks: Coping with few data and the training sample order
André Teixeira Lopes, Edilson de Aguiar, Alberto F. De Souza, and Thiago Oliveira-Santos · 2016
Earlier work this paper cites.
Low data drug discovery with one-shot learning
Han Altae-Tran, Bharath Ramsundar, Aneesh S. Pappu, and Vijay Pande · 2017
Earlier work this paper cites.
Feature selection in machine learning: A new perspective
Jie Cai, Jiawei Luo, Shulin Wang, and Sheng Yang · 2017
Cited alongside, same era.
Machine learning: New tool in the box
Lenka Zdeborová · 2017
Cited alongside, same era.
Machine learning based interatomic potential for amorphous carbon
Volker L Deringer and Gábor Csányi · 2017
Cited alongside, same era.
Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
Cited alongside, same era.
Information estimation using nonparametric copulas
Houman Safaai, Arno Onken, Christopher D. Harvey, and Stefano Panzeri · 2018
Cited alongside, same era.
Machine learning for molecular and materials science
Keith T Butler, Daniel W Davies, Hugh Cartwright, Olexandr Isayev, and Aron Walsh · 2018
On machine learning force fields for metallic nanoparticles
Claudio Zeni, Kevin Rossi, A Glielmo, and Francesca Baletto · 2019
Later among the works it cites.
Generalizing from a few examples: A survey on few-shot learning
Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni · 2020
Later among the works it cites.
Handling incomplete heterogeneous data using vaes
Alfredo Nazábal, Pablo M. Olmos, Zoubin Ghahramani, and Isabel Valera · 2020
Later among the works it cites.
COVID-19 Data Hub
Emanuele Guidotti and David Ardia · 2020
Later among the works it cites.
Variation in government responses to covid-19
Thomas Hale, Anna Petherick, Toby Phillips, and Samuel Webster · 2020
Later among the works it cites.
Ranking the effectiveness of worldwide covid-19 government interventions
Nils Haug, Lukas Geyrhofer, Alessandro Londei, Elma Dervic, Amélie Desvars-Larrive, Vittorio Loreto, Beate Pinior, Stefan Thurner, and Peter Klimek · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Efficient nonparametric n n -body force fields from machine learning
A Glielmo, Claudio Zeni, and A De Vita · 2018
Cited alongside, same era.
Predicting materials properties with little data using shotgun transfer learning
Hironao Yamada, Chang Liu, Stephen Wu, Yukinori Koyama, Shenghong Ju, Junichiro Shiomi, Junko Morikawa, and Ryo Yoshida · 2019
Cited alongside, same era.
A survey on image data augmentation for deep learning
Connor Shorten and Taghi M. Khoshgoftaar · 2019
Cited alongside, same era.
Feature selection for text classification: A review
Xuelian Deng, Yuqing Li, Jian Weng, and Jilian Zhang · 2019
Cited alongside, same era.
Deep Metric Learning: A Survey
Kaya and Bilge · 2019
Cited alongside, same era.
Inferring causation from time series in earth system sciences
Jakob Runge, Sebastian Bathiany, Erik Bollt, Gustau Camps-Valls, Dim Coumou, Ethan Deyle, Clark Glymour, Marlene Kretschmer, Miguel D Mahecha, Jordi Muñoz-Marí, et al · 2019
Cited alongside, same era.
Later among the works it cites.
The effect of large-scale anti-contagion policies on the covid-19 pandemic
Solomon Hsiang, Daniel Allen, Sébastien Annan-Phan, Kendon Bell, Ian Bolliger, Trinetta Chong, Hannah Druckenmiller, Luna Yue Huang, Andrew Hultgren, Emma Krasovich, Peiley Lau, Jaecheol Lee, Esther Rolf, Jeanette Tseng, and Tiffany Wu · 2020
Later among the works it cites.
Estimating the effects of non-pharmaceutical interventions on covid-19 in europe
Seth Flaxman, Swapnil Mishra, Axel Gandy, H. Juliette T. Unwin, Thomas A. Mellan, Helen Coupland, Charles Whittaker, Harrison Zhu, Tresnia Berah, Jeffrey W. Eaton, Mélodie Monod, Pablo N. Perez-Guzman, Nora Schmit, Lucia Cilloni, Kylie E. C. Ainslie, Marc Baguelin, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Laura V. Cooper, Zulma Cucunubá, Gina Cuomo-Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine L. van Elsland, Richard G. FitzJohn, Katy A. M. Gaythorpe, Lily Geidelberg, Nicholas C. Grassly, William D. Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, Edward Knock, Daniel J. Laydon, Gemma Nedjati-Gilani, Pierre Nouvellet, Kris V. Parag, Igor Siveroni, Hayley A. Thompson, Robert Verity, Erik Volz, Caroline E. Walters, Haowei Wang, Yuanrong Wang, Oliver J. Watson, Peter Winskill, Xiaoyue Xi, Patrick G. T. Walker, Azra C. Ghani, Christl A. Donnelly, Steven Riley, Michaela A. C. Vollmer, Neil M. Ferguson, Lucy C. Okell, Samir Bhatt, and Imperial College COVID-19 Response Team · 2020
Later among the works it cites.
The effect of interventions on COVID-19
Kristian Soltesz, Fredrik Gustafsson, Toomas Timpka, Joakim Jaldén, Carl Jidling, Albin Heimerson, Thomas B Schön, Armin Spreco, Joakim Ekberg, Örjan Dahlström, Fredrik Bagge Carlson, Anna Jöud, and Bo Bernhardsson · 2020
Later among the works it cites.
Machine Learning Meets Quantum Physics
Kristof T Schütt, Stefan Chmiela, O A von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, and K R Müller · 2020
Later among the works it cites.
Fast differentiable sorting and ranking
Mathieu Blondel, Olivier Teboul, Quentin Berthet, and Josip Djolonga · 2020
Later among the works it cites.
Inferring the effectiveness of government interventions against covid-19
Jan M. Brauner, Sören Mindermann, Mrinank Sharma, David Johnston, John Salvatier, Tomáš Gavenčiak, Anna B. Stephenson, Gavin Leech, George Altman, Vladimir Mikulik, Alexander John Norman, Joshua Teperowski Monrad, Tamay Besiroglu, Hong Ge, Meghan A. Hartwick, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal, and Jan Kulveit · 2021
Closest in time.
The role of feature space in atomistic learning
Alexander Goscinski, Guillaume Fraux, Giulio Imbalzano, and Michele Ceriotti · 2021
Closest in time.
Code: https://github.com/sissa-data-science/DADApy , Documentation: https://dadapy.readthedocs.io , 2022
Dadapy: Distance-based analysis of data-manifolds in python · 2022
Closest in time.
DADApy: Distance-based Analysis of DAta-manifolds in Python
Aldo Glielmo, Iuri Macocco, Diego Doimo, Matteo Carli, Claudio Zeni, Romina Wild, Maria d’Errico, Alex Rodriguez, and Laio Alessandro · 2022
Closest in time.