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cs.LG2024
Generating Rectifiable Measures through Neural Networks
Erwin Riegler, Alex Bühler, Yang Pan +1
We derive universal approximation results for the class of (countably) -rectifiable measures. Specifically, we prove that -rectifiable measures can be approximated as push-fo…
cs.AI2024
Cellular automata, many-valued logic, and deep neural networks
Yani Zhang, Helmut Bölcskei
We develop a theory characterizing the fundamental capability of deep neural networks to learn, from evolution traces, the logical rules governing the behavior of cellular automata…
cs.LG2024
Extracting Formulae in Many-Valued Logic from Deep Neural Networks
Yani Zhang, Helmut Bölcskei
We propose a new perspective on deep ReLU networks, namely as circuit counterparts of Lukasiewicz infinite-valued logic -- a many-valued (MV) generalization of Boolean logic. An al…