10 citations · 12 across the 3 of their papers we have counts for
6 papers
What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization
Maximilian Böther, Otto Kißig, Martin Taraz +3
Combinatorial optimization lies at the core of many real-world problems. Especially since the rise of graph neural networks (GNNs), the deep learning community has been developing…
Learning Languages in the Limit from Positive Information with Finitely Many Memory Changes
Timo Kötzing, Karen Seidel
We investigate learning collections of languages from texts by an inductive inference machine with access to the current datum and a bounded memory in form of states. Such a bounde…
Learning Half-Spaces and other Concept Classes in the Limit with Iterative Learners
Ardalan Khazraei, Timo Kötzing, Karen Seidel
In order to model an efficient learning paradigm, iterative learning algorithms access data one by one, updating the current hypothesis without regress to past data. Past research…
Random Subgroups of Rationals
Ziyuan Gao, Sanjay Jain, Bakhadyr Khoussainov +4
This paper introduces and studies a notion of \emph{algorithmic randomness} for subgroups of rationals. Given a randomly generated additive subgroup of rationals, two main…
Counting Homomorphisms to Trees Modulo a Prime
Andreas Göbel, J. A. Gregor Lagodzinski, Karen Seidel
Many important graph theoretic notions can be encoded as counting graph homomorphism problems, such as partition functions in statistical physics, in particular, independent sets a…
Learning from Informants: Relations between Learning Success Criteria
Martin Aschenbach, Timo Kötzing, Karen Seidel
Learning from positive and negative information, so-called \emph{informants}, being one of the models for human and machine learning introduced by E.~M.~Gold, is investigated. Part…