Search Results for author: Patryk Wielopolski

Found 6 papers, 3 papers with code

Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels

no code implementations27 May 2024 Patryk Wielopolski, Oleksii Furman, Jerzy Stefanowski, Maciej Zięba

By offering a cohesive solution to the optimization and plausibility challenges in GCEs, our work significantly advances the interpretability and accountability of AI models, marking a step forward in the pursuit of transparent AI.


Probabilistically Plausible Counterfactual Explanations with Normalizing Flows

no code implementations27 May 2024 Patryk Wielopolski, Oleksii Furman, Jerzy Stefanowski, Maciej Zięba

PPCEF advances beyond existing methods by combining a probabilistic formulation that leverages the data distribution with the optimization of plausibility within a unified framework.

counterfactual Fairness +1

Modeling Uncertainty in Personalized Emotion Prediction with Normalizing Flows

1 code implementation10 Dec 2023 Piotr Miłkowski, Konrad Karanowski, Patryk Wielopolski, Jan Kocoń, Przemysław Kazienko, Maciej Zięba

It may be solved by Personalized Natural Language Processing (PNLP), where the model exploits additional information about the reader to make more accurate predictions.

Emotion Recognition

TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression

no code implementations8 Jun 2022 Patryk Wielopolski, Maciej Zięba

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.


Flow Plugin Network for conditional generation

1 code implementation7 Oct 2021 Patryk Wielopolski, Michał Koperski, Maciej Zięba

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.

Conditional Image Generation Face Generation +2

PluGeN: Multi-Label Conditional Generation From Pre-Trained Models

1 code implementation18 Sep 2021 Maciej Wołczyk, Magdalena Proszewska, Łukasz Maziarka, Maciej Zięba, Patryk Wielopolski, Rafał Kurczab, Marek Śmieja

Modern generative models achieve excellent quality in a variety of tasks including image or text generation and chemical molecule modeling.

Attribute Text Generation

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