16 citations · 30 across the 5 of their papers we have counts for
5 papers
Impact Assessment of Missing Data in Model Predictions for Earth Observation Applications
Francisco Mena, Diego Arenas, Marcela Charfuelan +2
Earth observation (EO) applications involving complex and heterogeneous data sources are commonly approached with machine learning models. However, there is a common assumption tha…
Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level
Deepak Pathak, Miro Miranda, Francisco Mena +11
We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions. We use hig…
A Comparative Assessment of Multi-view fusion learning for Crop Classification
Francisco Mena, Diego Arenas, Marlon Nuske +1
With a rapidly increasing amount and diversity of remote sensing (RS) data sources, there is a strong need for multi-view learning modeling. This is a complex task when considering…
Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing
Ricardo Ñanculef, Francisco Mena, Antonio Macaluso +2
Semantic hashing is an emerging technique for large-scale similarity search based on representing high-dimensional data using similarity-preserving binary codes used for efficient…
Evaluating Bregman Divergences for Probability Learning from Crowd
F. A. Mena, R. Ñanculef
The crowdsourcing scenarios are a good example of having a probability distribution over some categories showing what the people in a global perspective thinks. Learn a predictive…