2 citations · 3 across the 10 of their papers we have counts for
4 papers · 1 filter
Prime and Modulate Learning: Generation of forward models with signed back-propagation and environmental cues
Sama Daryanavard, Bernd Porr
Deep neural networks employing error back-propagation for learning can suffer from exploding and vanishing gradient problems. Numerous solutions have been proposed such as normalis…
Sign and Relevance Learning
Sama Daryanavard, Bernd Porr
Standard models of biologically realistic or biologically inspired reinforcement learning employ a global error signal, which implies the use of shallow networks. On the other hand…
Reinforcement Learning
Olivier Buffet, Olivier Pietquin, Paul Weng
Reinforcement learning (RL) is a general framework for adaptive control, which has proven to be efficient in many domains, e.g., board games, video games or autonomous vehicles. In…
Closed-loop deep learning: generating forward models with back-propagation
Sama Daryanavard, Bernd Porr
A reflex is a simple closed loop control approach which tries to minimise an error but fails to do so because it will always react too late. An adaptive algorithm can use this erro…