Zobrazeno 1 - 10
of 565
pro vyhledávání: '"Wielopolski P"'
Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels
Growing regulatory and societal pressures demand increased transparency in AI, particularly in understanding the decisions made by complex machine learning models. Counterfactual Explanations (CFs) have emerged as a promising technique within Explain
Externí odkaz:
http://arxiv.org/abs/2405.17642
We present PPCEF, a novel method for generating probabilistically plausible counterfactual explanations (CFs). PPCEF advances beyond existing methods by combining a probabilistic formulation that leverages the data distribution with the optimization
Externí odkaz:
http://arxiv.org/abs/2405.17640
Autor:
Miłkowski, Piotr, Karanowski, Konrad, Wielopolski, Patryk, Kocoń, Jan, Kazienko, Przemysław, Zięba, Maciej
Designing predictive models for subjective problems in natural language processing (NLP) remains challenging. This is mainly due to its non-deterministic nature and different perceptions of the content by different humans. It may be solved by Persona
Externí odkaz:
http://arxiv.org/abs/2312.06034
Autor:
Anouk Corbeau, Pien van Gastel, Piotr A. Wielopolski, Nick de Jong, Carien L. Creutzberg, Uulke A. van der Heide, Stephanie M. de Boer, Eleftheria Astreinidou
Publikováno v:
Physics and Imaging in Radiation Oncology, Vol 32, Iss , Pp 100651- (2024)
Bone marrow (BM) damage due to chemoradiotherapy can increase BM fat in cervical cancer patients. Water-fat magnetic resonance (MR) scans were performed on a phantom and a healthy female volunteer to validate proton density fat fraction accuracy, rep
Externí odkaz:
https://doaj.org/article/813dade2a94846bfadd3f6abe1541c7e
Autor:
Wielopolski, Patryk, Zięba, Maciej
The tree-based ensembles are known for their outstanding performance in classification and regression problems characterized by feature vectors represented by mixed-type variables from various ranges and domains. However, considering regression probl
Externí odkaz:
http://arxiv.org/abs/2206.04140
Autor:
de Feyter Pim, Wielopolski Piotr, Regar Evelyn, Schulz Carl, Akkerhuis Martijn, Moelker Adriaan, Rossi Alexia, Springeling Tirza, van Geuns Robert-Jan
Publikováno v:
Journal of Cardiovascular Magnetic Resonance, Vol 13, Iss Suppl 1, p P108 (2011)
Externí odkaz:
https://doaj.org/article/4acb942f348a4d698a0f039087ad047a
Generative models have gained many researchers' attention in the last years resulting in models such as StyleGAN for human face generation or PointFlow for the 3D point cloud generation. However, by default, we cannot control its sampling process, i.
Externí odkaz:
http://arxiv.org/abs/2110.04081
Autor:
Wołczyk, Maciej, Proszewska, Magdalena, Maziarka, Łukasz, Zięba, Maciej, Wielopolski, Patryk, Kurczab, Rafał, Śmieja, Marek
Modern generative models achieve excellent quality in a variety of tasks including image or text generation and chemical molecule modeling. However, existing methods often lack the essential ability to generate examples with requested properties, suc
Externí odkaz:
http://arxiv.org/abs/2109.09011
Publikováno v:
Entropy, Vol 26, Iss 7, p 593 (2024)
We introduce NodeFlow, a flexible framework for probabilistic regression on tabular data that combines Neural Oblivious Decision Ensembles (NODEs) and Conditional Continuous Normalizing Flows (CNFs). It offers improved modeling capabilities for arbit
Externí odkaz:
https://doaj.org/article/3d7724cabe1f45f3ba2039c5e0c13468
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