Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Schwöbel, Pola"'
Autor:
Schwöbel, Pola, Franceschi, Luca, Zafar, Muhammad Bilal, Vasist, Keerthan, Malhotra, Aman, Shenhar, Tomer, Tailor, Pinal, Yilmaz, Pinar, Diamond, Michael, Donini, Michele
fmeval is an open source library to evaluate large language models (LLMs) in a range of tasks. It helps practitioners evaluate their model for task performance and along multiple responsible AI dimensions. This paper presents the library and exposes
Externí odkaz:
http://arxiv.org/abs/2407.12872
The issue of popularity bias -- where popular items are disproportionately recommended, overshadowing less popular but potentially relevant items -- remains a significant challenge in recommender systems. Recent advancements have seen the integration
Externí odkaz:
http://arxiv.org/abs/2406.01285
Large language models (LLMs) encode vast amounts of world knowledge. However, since these models are trained on large swaths of internet data, they are at risk of inordinately capturing information about dominant groups. This imbalance can propagate
Externí odkaz:
http://arxiv.org/abs/2310.14777
Autor:
Schwöbel, Pola, Remmers, Peter
In recent years, the idea of formalising and modelling fairness for algorithmic decision making (ADM) has advanced to a point of sophisticated specialisation. However, the relations between technical (formalised) and ethical discourse on fairness are
Externí odkaz:
http://arxiv.org/abs/2203.06038
Data augmentation is often used to incorporate inductive biases into models. Traditionally, these are hand-crafted and tuned with cross validation. The Bayesian paradigm for model selection provides a path towards end-to-end learning of invariances u
Externí odkaz:
http://arxiv.org/abs/2106.07512
Spatial Transformer Networks (STNs) estimate image transformations that can improve downstream tasks by `zooming in' on relevant regions in an image. However, STNs are hard to train and sensitive to mis-predictions of transformations. To circumvent t
Externí odkaz:
http://arxiv.org/abs/2004.03637
Autor:
Schwöbel, Pola Elisabeth
Publikováno v:
Schwöbel, P E 2022, Learned Data Augmentation for Bias Correction . Technical University of Denmark .
This thesis consists of three independent pieces of research that can be divided into two subject groups. The first block of topics is invariance learning and learned data augmentation (Paper 1 and 2 presented in Chapter 3 and 4, respectively). Paper
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1202::5028981c3f3dbb2573f5549a56338605
https://orbit.dtu.dk/en/publications/2f62b804-5646-472f-9749-0a9165f69fd3
https://orbit.dtu.dk/en/publications/2f62b804-5646-472f-9749-0a9165f69fd3