Search Results for author:
Found 5 papers, 1 papers with code
What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization
Second, using our benchmark suite, we conduct an in-depth analysis of the popular guided tree search algorithm by Li et al. [NeurIPS 2018], testing various configurations on small and large synthetic and real-world graphs.
What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization
Second, using our benchmark suite, we conduct an in-depth analysis of the popular guided tree search algorithm by Li et al. [NeurIPS 2018], testing various configurations on small and large synthetic and real-world graphs.
Learning Languages in the Limit from Positive Information with Finitely Many Memory Changes
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.
Learning Half-Spaces and other Concept Classes in the Limit with Iterative Learners
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.
Learning from Informants: Relations between Learning Success Criteria
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.
