1 citations · 2 across the 4 of their papers we have counts for
5 papers
Neural Combinatorial Logic Circuit Synthesis from Input-Output Examples
Peter Belcak, Roger Wattenhofer
We propose a novel, fully explainable neural approach to synthesis of combinatorial logic circuits from input-output examples. The carrying advantage of our method is that it readi…
A Neural Model for Regular Grammar Induction
Peter Belcák, David Hofer, Roger Wattenhofer
Grammatical inference is a classical problem in computational learning theory and a topic of wider influence in natural language processing. We treat grammars as a model of computa…
Periodic Extrapolative Generalisation in Neural Networks
Peter Belcák, Roger Wattenhofer
The learning of the simplest possible computational pattern -- periodicity -- is an open problem in the research of strong generalisation in neural networks. We formalise the probl…
FACT: Learning Governing Abstractions Behind Integer Sequences
Peter Belcák, Ard Kastrati, Flavio Schenker +1
Integer sequences are of central importance to the modeling of concepts admitting complete finitary descriptions. We introduce a novel view on the learning of such concepts and lay…
The LL(finite) strategy for optimal LL(k) parsing
Peter Belcak
The LL(finite) parsing strategy for parsing of LL(k) grammars where k needs not to be known is presented. The strategy parses input in linear time, uses arbitrary but always minima…