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Why don't you do it right? analysing annotators' disagreement in subjective tasks
Annotators' disagreement in linguistic data has been recently the focus of multiple initiatives
aimed at raising awareness on issues related to 'majority voting'when aggregating diverging …
aimed at raising awareness on issues related to 'majority voting'when aggregating diverging …
Deep dominance-how to properly compare deep neural models
Abstract Comparing between Deep Neural Network (DNN) models based on their
performance on unseen data is crucial for the progress of the NLP field. However, these …
performance on unseen data is crucial for the progress of the NLP field. However, these …
A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?
The ability to detect unfamiliar or unexpected images is essential for safe deployment of
computer vision systems. In the context of classification the task of detecting images outside …
computer vision systems. In the context of classification the task of detecting images outside …
Calibrating large language models using their generations only
Context-aware attention layers coupled with optimal transport domain adaptation and multimodal fusion methods for recognizing dementia from spontaneous speech
Alzheimer's disease (AD) constitutes a complex neurocognitive disease and is the main
cause of dementia. Although many studies have been proposed targeting at diagnosing …
cause of dementia. Although many studies have been proposed targeting at diagnosing …
[HTML][HTML] Are you sure it's an artifact? Artifact detection and uncertainty quantification in histological images
Modern cancer diagnostics involves extracting tissue specimens from suspicious areas and
conducting histotechnical procedures to prepare a digitized glass slide, called Whole Slide …
conducting histotechnical procedures to prepare a digitized glass slide, called Whole Slide …
Exploring predictive uncertainty and calibration in NLP: A study on the impact of method & data scarcity
We investigate the problem of determining the predictive confidence (or, conversely,
uncertainty) of a neural classifier through the lens of low-resource languages. By training …
uncertainty) of a neural classifier through the lens of low-resource languages. By training …
Not all layers are equally as important: Every layer counts BERT
This paper introduces a novel modification of the transformer architecture, tailored for the
data-efficient pretraining of language models. This aspect is evaluated by participating in the …
data-efficient pretraining of language models. This aspect is evaluated by participating in the …
[HTML][HTML] A multivariable sensor-agnostic framework for spatio-temporal air quality forecasting based on Deep Learning
Recently, air quality has become a major concern for the protection of the environment and
the well-being of people. Air pollution is a key proxy of the quality of life in any city and is …
the well-being of people. Air pollution is a key proxy of the quality of life in any city and is …