18 citations · 68 across the 16 of their papers we have counts for
7 papers · 1 filter
Proceedings of the NeurIPS 2021 Workshop on Machine Learning for the Developing World: Global Challenges
Paula Rodriguez Diaz, Tejumade Afonja, Konstantin Klemmer +4
These are the proceedings of the 5th workshop on Machine Learning for the Developing World (ML4D), held as part of the Thirty-fifth Conference on Neural Information Processing Syst…
GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy
Teddy Cunningham, Konstantin Klemmer, Hongkai Wen +1
Synthetic data generation is a fundamental task for many data management and data science applications. Spatial data is of particular interest, and its sensitive nature often leads…
Positional Encoder Graph Neural Networks for Geographic Data
Konstantin Klemmer, Nathan Safir, Daniel B. Neill
Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data. However, they often rely on Euclidean distances to construct the input g…
SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss
Konstantin Klemmer, Tianlin Xu, Beatrice Acciaio +1
From ecology to atmospheric sciences, many academic disciplines deal with data characterized by intricate spatio-temporal complexities, the modeling of which often requires special…
Proceedings of the NeurIPS 2020 Workshop on Machine Learning for the Developing World: Improving Resilience
Tejumade Afonja, Konstantin Klemmer, Aya Salama +3
These are the proceedings of the 4th workshop on Machine Learning for the Developing World (ML4D), held as part of the Thirty-fourth Conference on Neural Information Processing Sys…
Auxiliary-task learning for geographic data with autoregressive embeddings
Konstantin Klemmer, Daniel B. Neill
Machine learning is gaining popularity in a broad range of areas working with geographic data, such as ecology or atmospheric sciences. Here, data often exhibit spatial effects, wh…
Augmenting correlation structures in spatial data using deep generative models
Konstantin Klemmer, Adriano Koshiyama, Sebastian Flennerhag
State-of-the-art deep learning methods have shown a remarkable capacity to model complex data domains, but struggle with geospatial data. In this paper, we introduce SpaceGAN, a no…