Zobrazeno 1 - 10
of 448
pro vyhledávání: '"Gosiewska A"'
Autor:
Biecek, Przemysław, Chlebus, Marcin, Gajda, Janusz, Gosiewska, Alicja, Kozak, Anna, Ogonowski, Dominik, Sztachelski, Jakub, Wojewnik, Piotr
Rapid development of advanced modelling techniques gives an opportunity to develop tools that are more and more accurate. However as usually, everything comes with a price and in this case, the price to pay is to loose interpretability of a model whi
Externí odkaz:
http://arxiv.org/abs/2104.06735
Autor:
Yong Mao, Nikita John, Nicole M. Protzman, Desiree Long, Raja Sivalenka, Shamshad Azimi, Brandon Mirabile, Robert Pouliot, Anna Gosiewska, Robert J. Hariri, Stephen A. Brigido
Publikováno v:
Journal of Materials Science: Materials in Medicine, Vol 34, Iss 7, Pp 1-14 (2023)
Abstract Differences in scaffold design have the potential to influence cell-scaffold interactions. This study sought to determine whether a tri-layer design influences the cellular function of human tenocytes in vitro. The single-layer decellularize
Externí odkaz:
https://doaj.org/article/1d2d360757434f0197b06bbeab2c5e42
A major requirement for credit scoring models is to provide a maximally accurate risk prediction. Additionally, regulators demand these models to be transparent and auditable. Thus, in credit scoring, very simple predictive models such as logistic re
Externí odkaz:
http://arxiv.org/abs/2009.13384
The growing availability of data and computing power fuels the development of predictive models. In order to ensure the safe and effective functioning of such models, we need methods for exploration, debugging, and validation. New methods and tools f
Externí odkaz:
http://arxiv.org/abs/2009.13248
Publikováno v:
Nature Machine Intelligence, Vol 4,792-800 (2022)
Benchmarks for the evaluation of model performance play an important role in machine learning. However, there is no established way to describe and create new benchmarks. What is more, the most common benchmarks use performance measures that share se
Externí odkaz:
http://arxiv.org/abs/2006.02293
Autor:
Gosiewska, Alicja, Biecek, Przemyslaw
Complex black-box predictive models may have high performance, but lack of interpretability causes problems like lack of trust, lack of stability, sensitivity to concept drift. On the other hand, achieving satisfactory accuracy of interpretable model
Externí odkaz:
http://arxiv.org/abs/2002.04267
Autor:
Gosiewska, Alicja, Bakala, Mateusz, Woznica, Katarzyna, Zwolinski, Maciej, Biecek, Przemyslaw
The most important part of model selection and hyperparameter tuning is the evaluation of model performance. The most popular measures, such as AUC, F1, ACC for binary classification, or RMSE, MAD for regression, or cross-entropy for multilabel class
Externí odkaz:
http://arxiv.org/abs/1908.09213
Autor:
Gosiewska, Alicja, Biecek, Przemyslaw
Explainable Artificial Intelligence (XAI)has received a great deal of attention recently. Explainability is being presented as a remedy for the distrust of complex and opaque models. Model agnostic methods such as LIME, SHAP, or Break Down promise in
Externí odkaz:
http://arxiv.org/abs/1903.11420
Complex black-box predictive models may have high accuracy, but opacity causes problems like lack of trust, lack of stability, sensitivity to concept drift. On the other hand, interpretable models require more work related to feature engineering, whi
Externí odkaz:
http://arxiv.org/abs/1902.11035
Autor:
Gosiewska, Alicja, Biecek, Przemyslaw
Machine learning models have spread to almost every area of life. They are successfully applied in biology, medicine, finance, physics, and other fields. With modern software it is easy to train even a~complex model that fits the training data and re
Externí odkaz:
http://arxiv.org/abs/1809.07763