5 citations · 9 across the 9 of their papers we have counts for
9 papers
Confidence Calibration in Large Language Model-Based Entity Matching
Iris Kamsteeg, Juan Cardenas-Cartagena, Floris van Beers +3
This research aims to explore the intersection of Large Language Models and confidence calibration in Entity Matching. To this end, we perform an empirical study to compare baselin…
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…
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…
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),…
Mapping Transformer Leveraged Embeddings for Cross-Lingual Document Representation
Tsegaye Misikir Tashu, Eduard-Raul Kontos, Matthia Sabatelli +1
Recommendation systems, for documents, have become tools to find relevant content on the Web. However, these systems have limitations when it comes to recommending documents in lan…
Machine Learning Students Overfit to Overfitting
Matias Valdenegro-Toro, Matthia Sabatelli
Overfitting and generalization is an important concept in Machine Learning as only models that generalize are interesting for general applications. Yet some students have trouble l…