61 citations · 120 across the 10 of their papers we have counts for
14 papers
ActUp: Analyzing and Consolidating tSNE and UMAP
Andrew Draganov, Jakob Rødsgaard Jørgensen, Katrine Scheel Nellemann +4
tSNE and UMAP are popular dimensionality reduction algorithms due to their speed and interpretable low-dimensional embeddings. Despite their popularity, however, little work has be…
GiDR-DUN; Gradient Dimensionality Reduction -- Differences and Unification
Andrew Draganov, Tyrus Berry, Jakob Rødsgaard Jørgensen +3
TSNE and UMAP are two of the most popular dimensionality reduction algorithms due to their speed and interpretable low-dimensional embeddings. However, while attempts have been mad…
Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding
Joachim Nyborg, Charlotte Pelletier, Ira Assent
Large-scale crop type classification is a task at the core of remote sensing efforts with applications of both economic and ecological importance. Current state-of-the-art deep lea…
UCoDe: Unified Community Detection with Graph Convolutional Networks
Atefeh Moradan, Andrew Draganov, Davide Mottin +1
Community detection finds homogeneous groups of nodes in a graph. Existing approaches either partition the graph into disjoint, non-overlapping, communities, or determine only over…
Weakly-Supervised Cloud Detection with Fixed-Point GANs
Joachim Nyborg, Ira Assent
The detection of clouds in satellite images is an essential preprocessing task for big data in remote sensing. Convolutional neural networks (CNNs) have greatly advanced the state-…
TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation
Joachim Nyborg, Charlotte Pelletier, Sébastien Lefèvre +1
The recent developments of deep learning models that capture complex temporal patterns of crop phenology have greatly advanced crop classification from Satellite Image Time Series…