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Found 14 papers, 2 papers with code
MOD-CL: Multi-label Object Detection with Constrained Loss
In Task 1, we introduce the Corrector Model and Blender Model, two new models that follow after the object detection process, aiming to generate a more constrained output.
Structured World Representations in Maze-Solving Transformers
Transformer models underpin many recent advances in practical machine learning applications, yet understanding their internal behavior continues to elude researchers.
Bounded Combinatorial Reconfiguration with Answer Set Programming
The resulting recongo solver covers all metrics of the solver track in the most recent international competition on combinatorial reconfiguration (CoRe Challenge 2022).
Towards end-to-end ASP computation
We propose an end-to-end approach for answer set programming (ASP) and linear algebraically compute stable models satisfying given constraints.
Learning State Transition Rules from Hidden Layers of Restricted Boltzmann Machines
Experimental results show that our method can learn the dynamics of those physical systems as state transition rules between hidden variables and can predict unobserved future states from observed state transitions.
Action Languages Based Actual Causality for Computational Ethics: a Sound and Complete Implementation in ASP
Although moral responsibility is not circumscribed by causality, they are both closely intermixed.
Learning First-Order Rules with Differentiable Logic Program Semantics
We map the symbolic forward-chained format of LPs into NN constraint functions consisting of operations between subsymbolic vector representations of atoms.
Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming
We propose a method for generating explainable rule sets from tree-ensemble learners using Answer Set Programming (ASP).
Partial Evaluation of Logic Programs in Vector Spaces
In this paper, we introduce methods of encoding propositional logic programs in vector spaces.
Characterization of Logic Program Revision as an Extension of Propositional Revision
In this paper, a constructive characterization of all rational LP revision operators is given in terms of orderings over propositional interpretations with some further conditions specific to SE interpretations.
Aspartame: Solving Constraint Satisfaction Problems with Answer Set Programming
We here develop an alternative approach based on ASP.
Post-Proceedings of the First International Workshop on Learning and Nonmonotonic Reasoning
On the other side, Inductive Logic Programming (ILP) realizes Machine Learning in logic programming, which provides a formal background to inductive learning and the techniques have been applied to the fields of relational learning and data mining.
Encoding Petri Nets in Answer Set Programming for Simulation Based Reasoning
However, we need to make extensions to the Petri Net model and also reason with multiple simulation runs and parallel state evolutions.
Encoding Higher Level Extensions of Petri Nets in Answer Set Programming
Answering realistic questions about biological systems and pathways similar to the ones used by text books to test understanding of students about biological systems is one of our long term research goals.
