no code implementations • 17 Apr 2024 • Nils Ole Breuer, Andreas Sauter, Majid Mohammadi, Erman Acar
One way to explain AI models is to elucidate the predictive importance of the input features for the AI model in general, also referred to as global explanations.
no code implementations • 13 Mar 2024 • Tuomas Varanka, Tapani Toivonen, Soumya Tripathy, Guoying Zhao, Erman Acar
In our approach a restoration model is personalized using a few images of the identity, leading to tailored restoration with respect to the identity while retaining fine-grained details.
no code implementations • 14 Feb 2024 • Catayoun Azarm, Erman Acar, Mickey van Zeelt
Online transaction fraud presents substantial challenges to businesses and consumers, risking significant financial losses.
1 code implementation • 30 Jan 2024 • Andreas W. M. Sauter, Nicolò Botteghi, Erman Acar, Aske Plaat
Causal discovery is the challenging task of inferring causal structure from data.
no code implementations • 23 Jan 2024 • Nicole Orzan, Erman Acar, Davide Grossi, Roxana Rădulescu
Understanding the emergence of cooperation in systems of computational agents is crucial for the development of effective cooperative AI.
1 code implementation • 1 Dec 2023 • Samantha Visbeek, Erman Acar, Floris den Hengst
There is a growing demand for explainable, transparent, and data-driven models within the domain of fraud detection.
no code implementations • 26 Jul 2023 • Bram Renting, Phillip Wozny, Robert Loftin, Claudia Wieners, Erman Acar
We present a critical analysis of the simulation framework RICE-N, an integrated assessment model (IAM) for evaluating the impacts of climate change on the economy.
no code implementations • 26 Jul 2023 • Phillip Wozny, Bram Renting, Robert Loftin, Claudia Wieners, Erman Acar
As our submission for track three of the AI for Global Climate Cooperation (AI4GCC) competition, we propose a negotiation protocol for use in the RICE-N climate-economic simulation.
no code implementations • 4 Jan 2023 • Erman Acar, Andrea De Domenico, Krishna Manoorkar, Mattia Panettiere
These algorithms use a single concept lattice for such a task, meaning that the set of features used for the categorization is fixed.
1 code implementation • 18 Jul 2022 • Andreas Sauter, Erman Acar, Vincent François-Lavet
Causal discovery is a major task with the utmost importance for machine learning since causal structures can enable models to go beyond pure correlation-based inference and significantly boost their performance.
1 code implementation • CVPR 2022 • Mina GhadimiAtigh, Julian Schoep, Erman Acar, Nanne van Noord, Pascal Mettes
For image segmentation, the current standard is to perform pixel-level optimization and inference in Euclidean output embedding spaces through linear hyperplanes.
no code implementations • 1 Jul 2020 • Erman Acar, Rafael Peñaloza
Influence diagrams (IDs) are well-known formalisms extending Bayesian networks to model decision situations under uncertainty.
no code implementations • 4 Jun 2020 • Emile van Krieken, Erman Acar, Frank van Harmelen
In this paper, we investigate how implications from the fuzzy logic literature behave in a differentiable setting.
no code implementations • 13 Mar 2020 • Renyan Feng, Erman Acar, Stefan Schlobach, Yisong Wang, Wanwei Liu
To address such a scenario in a principled way, we introduce a forgetting-based approach in CTL and show that it can be used to compute SNC and WSC of a property under a given model and over a given signature.
1 code implementation • 14 Feb 2020 • Emile van Krieken, Erman Acar, Frank van Harmelen
Finally, we empirically show that it is possible to use Differentiable Fuzzy Logics for semi-supervised learning, and compare how different operators behave in practice.
1 code implementation • 13 Aug 2019 • Emile van Krieken, Erman Acar, Frank van Harmelen
We introduce Differentiable Reasoning (DR), a novel semi-supervised learning technique which uses relational background knowledge to benefit from unlabeled data.