Self-supervised local learning rules learn the hidden hierarchical structure of high-dimensional data

Documenti analoghi

• Neural Langevin Machine: a local asymmetric learning rule can be creative
  di: Yu, Zhendong, et al.
  Pubblicazione: (2025)
• The dynamic interplay between in-context and in-weight learning in humans and neural networks
  di: Russin, Jacob, et al.
  Pubblicazione: (2024)
• Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning
  di: Chavlis, Spyridon, et al.
  Pubblicazione: (2024)
• ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks
  di: Li, Junyan, et al.
  Pubblicazione: (2025)
• Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics
  di: Mei, Jie, et al.
  Pubblicazione: (2025)