Machine-learnable Sets
In this study we present a formal definition of large discrete sets having, informally, three properties: their elements are easily recognized, easily generated, and the latter tasks are easily learned from examples. The formalism is specialized to sets of binary strings and a definition of "machine-learnability" based on the existence of a bounded-complexity Boolean autoencoder that fixes the elements of the set. We present experiments where the autoencoders are implemented by nets of Boolean threshold functions. Machine-learnability is demonstrated for Rorschach patterns (that may have reversed contrast in the mirrored half), and considerably "wilder" sets whose elements are only approx...
| Authors | Veit Elser, Manish Krishan Lal |
|---|---|
| Primary category | cs.LG |
| Published | 27 June 2026 |
| Last updated at source | 26 August 2026 |
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