NORACL dynamically grows network capacity via neurogenesis-inspired signals to achieve oracle-level continual learning performance without pre-specifying architecture size.
Reinforced continual learning
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
TACO reformulates neural implicit mapping as temporal consensus optimization to enable continual adaptation to scene changes without data replay or storage.
citing papers explorer
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NORACL: Neurogenesis for Oracle-free Resource-Adaptive Continual Learning
NORACL dynamically grows network capacity via neurogenesis-inspired signals to achieve oracle-level continual learning performance without pre-specifying architecture size.
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TACO: Temporal Consensus Optimization for Continual Neural Mapping
TACO reformulates neural implicit mapping as temporal consensus optimization to enable continual adaptation to scene changes without data replay or storage.