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cs.LG2026
Evolving Afferent Architectures: Biologically-inspired Models for Damage-Avoidance Learning
Wolfgang Maass, Sabine Janzen, Prajvi Saxena +1
We introduce Afferent Learning, a framework that produces Computational Afferent Traces (CATs) as adaptive, internal risk signals for damage-avoidance learning. Inspired by biologi…
cs.LG2022
Deep Learning of Causal Structures in High Dimensions
Kai Lagemann, Christian Lagemann, Bernd Taschler +1
Recent years have seen rapid progress at the intersection between causality and machine learning. Motivated by scientific applications involving high-dimensional data, in particula…