27 citations · 31 across the 11 of their papers we have counts for
13 papers
Faithfulness Without Anchors: A Position Paper on the Structural Misalignments of Explainable AI Faithfulness Metrics for Feature Importance
Marco Zullich, Emily Schiller, Ivan Gentile +4
Explainable Artificial Intelligence (XAI) tools have often been presented as methods to increase trust in machine learning models by making aspects of their algorithms more human-u…
On the Properties of Feature Attribution for Supervised Contrastive Learning
Leonardo Arrighi, Julia Eva Belloni, Aurélie Gallet +3
Most Neural Networks (NNs) for classification are trained using Cross-Entropy as a loss function. This approach requires the model to have an explicit classification layer. However…
No Single Metric Tells the Whole Story: A Multi-Dimensional Evaluation Framework for Uncertainty Attributions
Emily Schiller, Teodor Chiaburu, Marco Zullich +1
Research on explainable AI (XAI) has frequently focused on explaining model predictions. More recently, methods have been proposed to explain prediction uncertainty by attributing…
Enhancing Tree Species Classification: Insights from YOLOv8 and Explainable AI Applied to TLS Point Cloud Projections
Adrian Straker, Paul Magdon, Marco Zullich +5
Aiming to advance research in the field of interpretability of deep learning models for tree species classification using TLS 3D point clouds we present insights in the classificat…
Uncertainty in Semantic Language Modeling with PIXELS
Stefania Radu, Marco Zullich, Matias Valdenegro-Toro
Pixel-based language models aim to solve the vocabulary bottleneck problem in language modeling, but the challenge of uncertainty quantification remains open. The novelty of this w…
Sparsity-Driven Plasticity in Multi-Task Reinforcement Learning
Aleksandar Todorov, Juan Cardenas-Cartagena, Rafael F. Cunha +2
Plasticity loss, a diminishing capacity to adapt as training progresses, is a critical challenge in deep reinforcement learning. We examine this issue in multi-task reinforcement l…
Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Review
Leonardo Arrighi, Ingrid Alves de Moraes, Marco Zullich +3
Artificial Intelligence (AI) has become essential for analyzing complex data and solving highly-challenging tasks. It is being applied across numerous disciplines beyond computer s…
Can Bayesian Neural Networks Explicitly Model Input Uncertainty?
Matias Valdenegro-Toro, Marco Zullich
Inputs to machine learning models can have associated noise or uncertainties, but they are often ignored and not modelled. It is unknown if Bayesian Neural Networks and their appro…
Large-image Object Detection for Fine-grained Recognition of Punches Patterns in Medieval Panel Painting
Josh Bruegger, Diana Ioana Catana, Vanja Macovaz +3
The attribution of the author of an art piece is typically a laborious manual process, usually relying on subjective evaluations of expert figures. However, there are some situatio…
Adaptive Prompt Tuning: Vision Guided Prompt Tuning with Cross-Attention for Fine-Grained Few-Shot Learning
Eric Brouwer, Jan Erik van Woerden, Gertjan Burghouts +2
Few-shot, fine-grained classification in computer vision poses significant challenges due to the need to differentiate subtle class distinctions with limited data. This paper prese…
Uncertainty Estimation for Super-Resolution using ESRGAN
Maniraj Sai Adapa, Marco Zullich, Matias Valdenegro-Toro
Deep Learning-based image super-resolution (SR) has been gaining traction with the aid of Generative Adversarial Networks. Models like SRGAN and ESRGAN are constantly ranked betwee…
Upside-Down Reinforcement Learning for More Interpretable Optimal Control
Juan Cardenas-Cartagena, Massimiliano Falzari, Marco Zullich +1
Model-Free Reinforcement Learning (RL) algorithms either learn how to map states to expected rewards or search for policies that can maximize a certain performance function. Model-…
Unified Uncertainties: Combining Input, Data and Model Uncertainty into a Single Formulation
Matias Valdenegro-Toro, Ivo Pascal de Jong, Marco Zullich
Modelling uncertainty in Machine Learning models is essential for achieving safe and reliable predictions. Most research on uncertainty focuses on output uncertainty (predictions),…