Fetching the paper…
Reading the bibliography…
Learning representations of geographical space is vital for any machine learning model that integrates geolocated data, spanning application domains such as remote sensing, ecology, or epidemiology.
Le krigeage universel (Universal kriging)
Georges Matheron · 1969
Earlier work this paper cites.
The pseudospectral approximation applied to the shallow water equations on a sphere
Philip E. Merilees · 1973
Earlier work this paper cites.
Fourier series on spheres
Steven A Orszag · 1974
Earlier work this paper cites.
Saturn’s magnetic field and magnetosphere
EJ Smith, L Davis Jr, DE Jones, P Jo Coleman Jr, DS Colburn, P Dyal, and CP Sonett · 1980
Earlier work this paper cites.
Kriging: A method of interpolation for geographical information systems
Margaret A Oliver and Richard Webster · 1990
Earlier work this paper cites.
Spherical harmonic lighting: The gritty details
Robin Green · 2003
Earlier work this paper cites.
Fibonacci grids: A novel approach to global modelling
Richard Swinbank and R James Purser · 2006
Earlier work this paper cites.
Compositional pattern producing networks: A novel abstraction of development
Kenneth O Stanley · 2007
Earlier work this paper cites.
Measurement of areas on a sphere using fibonacci and latitude–longitude lattices
Álvaro González · 2010
Earlier work this paper cites.
First goce gravity field models derived by three different approaches
Roland Pail, Sean Bruinsma, Federica Migliaccio, Christoph Förste, Helmut Goiginger, Wolf-Dieter Schuh, Eduard Höck, Mirko Reguzzoni, Jan Martin Brockmann, Oleg Abrikosov, Martin Veicherts, Thomas Fecher, Reinhard Mayrhofer, Ina Krasbutter, Fernando Sansò, and Carl Christian Tscherning · 2011
Earlier work this paper cites.
Birdsnap: Large-scale fine-grained visual categorization of birds
Thomas Berg, Jiongxin Liu, Seung Woo Lee, Michelle L Alexander, David W Jacobs, and Peter N Belhumeur · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Improving image classification with location context
Kevin Tang, Manohar Paluri, Li Fei-Fei, Rob Fergus, and Lubomir Bourdev · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
A simple yet effective baseline for 3d human pose estimation
Julieta Martinez, Rayat Hossain, Javier Romero, and James J Little · 2017
Cited alongside, same era.
Sympy: symbolic computing in python
Aaron Meurer, Christopher P. Smith, Mateusz Paprocki, Ondřej Čertík, Sergey B. Kirpichev, Matthew Rocklin, AMiT Kumar, Sergiu Ivanov, Jason K. Moore, Sartaj Singh, Thilina Rathnayake, Sean Vig, Brian E. Granger, Richard P. Muller, Francesco Bonazzi, Harsh Gupta, Shivam Vats, Fredrik Johansson, Fabian Pedregosa, Matthew J. Curry, Andy R. Terrel, Štěpán Roučka, Ashutosh Saboo, Isuru Fernando, Sumith Kulal, Robert Cimrman, and Anthony Scopatz · 2017
Cited alongside, same era.
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, and Andrew Gordon Wilson · 2018
Cited alongside, same era.
Generalised implicit neural representations
Daniele Grattarola and Pierre Vandergheynst · 2022
Later among the works it cites.
Intrinsic neural fields: Learning functions on manifolds
Lukas Koestler, Daniel Grittner, Michael Moeller, Daniel Cremers, and Zorah Lähner · 2022
Later among the works it cites.
A high-resolution canopy height model of the Earth
Nico Lang, Walter Jetz, Konrad Schindler, and Jan Dirk Wegner · 2022
Later among the works it cites.
A review of location encoding for GeoAI: methods and applications
Gengchen Mai, Krzysztof Janowicz, Yingjie Hu, Song Gao, Bo Yan, Rui Zhu, Ling Cai, and Ni Lao · 2022
Later among the works it cites.
TIML: Task-informed meta-learning for agriculture
Gabriel Tseng, Hannah Kerner, and David Rolnick · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The iNaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2018
Cited alongside, same era.
Optuna: A next-generation hyperparameter optimization framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 2019
Cited alongside, same era.
Geo-aware networks for fine-grained recognition
Grace Chu, Brian Potetz, Weijun Wang, Andrew Howard, Yang Song, Fernando Brucher, Thomas Leung, and Hartwig Adam · 2019
Cited alongside, same era.
PyTorch Lightning, March 2019
William Falcon and The PyTorch Lightning team · 2019
Cited alongside, same era.
Presence-only geographical priors for fine-grained image classification
Oisin Mac Aodha, Elijah Cole, and Pietro Perona · 2019
Cited alongside, same era.
Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
Cited alongside, same era.
Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2021
Cited alongside, same era.
Oussama Boussif, Ghait Boukachab, Dan Assouline, Stefano Massaroli, Tianle Yuan, Loubna Benabbou, and Yoshua Bengio · 2023
Closest in time.
Spatial implicit neural representations for global-scale species mapping
Elijah Cole, Grant Van Horn, Christian Lange, Alexander Shepard, Patrick Leary, Pietro Perona, Scott Loarie, and Oisin Mac Aodha · 2023
Closest in time.
Compressing multidimensional weather and climate data into neural networks
Langwen Huang and Torsten Hoefler · 2023
Closest in time.
Improving deep learning acoustic classifiers with contextual information for wildlife monitoring
Lorène Jeantet and Emmanuel Dufourq · 2023
Closest in time.
Positional encoder graph neural networks for geographic data
Konstantin Klemmer, Nathan S Safir, and Daniel B Neill · 2023
Closest in time.
Lightweight, pre-trained transformers for remote sensing timeseries
Gabriel Tseng, Ivan Zvonkov, Mirali Purohit, David Rolnick, and Hannah Kerner · 2023
Closest in time.