78 citations · 197 across the 22 of their papers we have counts for
10 papers · 1 filter
The Directed Prediction Change - Efficient and Trustworthy Fidelity Assessment for Local Feature Attribution Methods
Kevin Iselborn, David Dembinsky, Adriano Lucieri +1
The utility of an explanation method critically depends on its fidelity to the underlying machine learning model. Especially in high-stakes medical settings, clinicians and regulat…
Multi-modal Co-learning for Earth Observation: Enhancing single-modality models via modality collaboration
Francisco Mena, Dino Ienco, Cassio F. Dantas +2
Multi-modal co-learning is emerging as an effective paradigm in machine learning, enabling models to collaboratively learn from different modalities to enhance single-modality pred…
Informed Learning for Estimating Drought Stress at Fine-Scale Resolution Enables Accurate Yield Prediction
Miro Miranda, Marcela Charfuelan, Matias Valdenegro Toro +1
Water is essential for agricultural productivity. Assessing water shortages and reduced yield potential is a critical factor in decision-making for ensuring agricultural productivi…
Quantum-Assisted Correlation Clustering
Antonio Macaluso, Supreeth Mysore Venkatesh, Diego Arenas +2
This work introduces a hybrid quantum-classical method to correlation clustering, a graph-based unsupervised learning task that seeks to partition the nodes in a graph based on pai…
Can Multitask Learning Enhance Model Explainability?
Hiba Najjar, Bushra Alshbib, Andreas Dengel
Remote sensing provides satellite data in diverse types and formats. The usage of multimodal learning networks exploits this diversity to improve model performance, except that the…
Intrinsic Explainability of Multimodal Learning for Crop Yield Prediction
Hiba Najjar, Deepak Pathak, Marlon Nuske +1
Multimodal learning enables various machine learning tasks to benefit from diverse data sources, effectively mimicking the interplay of different factors in real-world applications…
i-QLS: Quantum-supported Algorithm for Least Squares Optimization in Non-Linear Regression
Supreeth Mysore Venkatesh, Antonio Macaluso, Diego Arenas +2
We propose an iterative quantum-assisted least squares (i-QLS) optimization method that leverages quantum annealing to overcome the scalability and precision limitations of prior q…
An Analysis of Temporal Dropout in Earth Observation Time Series for Regression Tasks
Miro Miranda, Francisco Mena, Andreas Dengel
Missing instances in time series data impose a significant challenge to deep learning models, particularly in regression tasks. In the Earth Observation field, satellite failure or…
On What Depends the Robustness of Multi-source Models to Missing Data in Earth Observation?
Francisco Mena, Diego Arenas, Miro Miranda +1
In recent years, the development of robust multi-source models has emerged in the Earth Observation (EO) field. These are models that leverage data from diverse sources to improve…
Missing Data as Augmentation in the Earth Observation Domain: A Multi-View Learning Approach
Francisco Mena, Diego Arenas, Andreas Dengel
Multi-view learning (MVL) leverages multiple sources or views of data to enhance machine learning model performance and robustness. This approach has been successfully used in the…