7 papers
What's in an Earth Embedding? An Explainability Analysis of Location Encoders
Livia Betti, Sebastian Ricke, Ivica Obadic +2
Geographic implicit neural representations (INRs) learn to map any coordinate on Earth to a location embedding, implicitly encoding geospatial data into the weights of a neural net…
OT on the Map: Quantifying Domain Shifts in Geographic Space
Haoran Zhang, Livia Betti, Konstantin Klemmer +2
In computer vision and machine learning for geographic data, out-of-domain generalization is a pervasive challenge, arising from uneven global data coverage and distribution shifts…
Mapping on a Budget: Optimizing Spatial Data Collection for ML
Livia Betti, Farooq Sanni, Gnouyaro Sogoyou +4
In applications across agriculture, ecology, and human development, machine learning with satellite imagery (SatML) is limited by the sparsity of labeled training data. While satel…
VC-Dimension of Hyperplanes over Finite Fields
Ruben Ascoli, Livia Betti, Justin Cheigh +8
Let be the -dimensional vector space over the finite field with elements. For a subset and a fixed nonzero ,…
Benfordness of Measurements Resulting from Box Fragmentation
Livia Betti, Irfan Durmić, Zoe McDonald +2
We make progress on a conjecture made by [DM], which states that the -dimensional frames of -dimensional boxes resulting from a fragmentation process satisfy Benford's law fo…
VC-Dimension and Distance Chains in
Ruben Ascoli, Livia Betti, Justin Cheigh +8
Given a domain and a collection of functions , the Vapnik-Chervonenkis (VC) dimension of measures its complexity in an appropriate s…
Distinct Angles and Angle Chains in Three Dimensions
Ruben Ascoli, Livia Betti, Jacob Lehmann Duke +6
In 1946, Erdős posed the distinct distance problem, which seeks to find the minimum number of distinct distances between pairs of points selected from any configuration of poin…