no code implementations • EMNLP 2021 • Simon Suster, Pieter Fivez, Pietro Totis, Angelika Kimmig, Jesse Davis, Luc De Raedt, Walter Daelemans

While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically.

no code implementations • 3 Apr 2023 • Pietro Totis, Angelika Kimmig, Luc De Raedt

The first contribution of this paper is a novel interpretation of probabilistic argumentation frameworks as probabilistic logic programs.

no code implementations • 8 Mar 2023 • Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve, Giuseppe Marra, Angelika Kimmig, Luc De Raedt

Probabilistic NeSy focuses on integrating neural networks with both logic and probability theory, which additionally allows learning under uncertainty.

no code implementations • 21 Feb 2023 • Pedro Zuidberg Dos Martires, Luc De Raedt, Angelika Kimmig

The resulting paradigm of probabilistic logic programming (PLP) and its programming languages owes much of its success to a declarative semantics, the so-called distribution semantics.

1 code implementation • 16 May 2022 • Rafael Kiesel, Pietro Totis, Angelika Kimmig

We introduce Second Level Algebraic Model Counting (2AMC) as a generic framework for these kinds of problems.

no code implementations • 15 Oct 2021 • Marc Roig Vilamala, Tianwei Xing, Harrison Taylor, Luis Garcia, Mani Srivastava, Lance Kaplan, Alun Preece, Angelika Kimmig, Federico Cerutti

We also demonstrate that our approach is capable of training even with a dataset that has a moderate proportion of noisy data.

no code implementations • 5 Oct 2021 • Pietro Totis, Angelika Kimmig, Luc De Raedt

Therefore, the key contribution of this paper are: a more general semantics for ProbLog programs, its implementation into a probabilistic programming framework for both inference and parameter learning, and a novel approach to probabilistic argumentation problems based on such framework.

1 code implementation • 22 Feb 2021 • Federico Cerutti, Lance M. Kaplan, Angelika Kimmig, Murat Sensoy

When collaborating with an AI system, we need to assess when to trust its recommendations.

no code implementations • 19 Sep 2020 • Francesco Ricca, Alessandra Russo, Sergio Greco, Nicola Leone, Alexander Artikis, Gerhard Friedrich, Paul Fodor, Angelika Kimmig, Francesca Lisi, Marco Maratea, Alessandra Mileo, Fabrizio Riguzzi

Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming.

no code implementations • 7 Sep 2020 • Marc Roig Vilamala, Harrison Taylor, Tianwei Xing, Luis Garcia, Mani Srivastava, Lance Kaplan, Alun Preece, Angelika Kimmig, Federico Cerutti

We demonstrate this comparing our approach against a pure neural network approach on a dataset based on Urban Sounds 8K.

no code implementations • 30 Mar 2020 • Sam Vente, Angelika Kimmig, Alun Preece, Federico Cerutti

In particular, we show our method significantly reduces the number of messages when an agreement is not possible.

no code implementations • 30 Mar 2020 • Sam Vente, Angelika Kimmig, Alun Preece, Federico Cerutti

Automated negotiation can be an efficient method for resolving conflict and redistributing resources in a coalition setting.

no code implementations • 18 Nov 2019 • Efthymia Tsamoura, Victor Gutierrez-Basulto, Angelika Kimmig

State-of-the-art inference approaches in probabilistic logic programming typically start by computing the relevant ground program with respect to the queries of interest, and then use this program for probabilistic inference using knowledge compilation and weighted model counting.

no code implementations • NeurIPS 2018 • Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester, Luc De Raedt

We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates.

1 code implementation • 20 Sep 2018 • Federico Cerutti, Lance Kaplan, Angelika Kimmig, Murat Sensoy

We enable aProbLog---a probabilistic logical programming approach---to reason in presence of uncertain probabilities represented as Beta-distributed random variables.

4 code implementations • NeurIPS 2018 • Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester, Luc De Raedt

We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates.

no code implementations • 24 Jul 2017 • Seyed Mehran Kazemi, Angelika Kimmig, Guy Van Den Broeck, David Poole

In this paper, we show that domain recursion can also be applied to models with existential quantifiers.

no code implementations • 11 Feb 2017 • Angelika Kimmig, Alex Memory, Renee J. Miller, Lise Getoor

In this appendix we provide additional supplementary material to "A Collective, Probabilistic Approach to Schema Mapping."

no code implementations • NeurIPS 2016 • Seyed Mehran Kazemi, Angelika Kimmig, Guy Van Den Broeck, David Poole

Statistical relational models provide compact encodings of probabilistic dependencies in relational domains, but result in highly intractable graphical models.

no code implementations • 10 Jul 2015 • Joris Renkens, Angelika Kimmig, Luc De Raedt

We introduce a lazy approach to the explanation-based approximation of probabilistic logic programs.

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