14 citations · 107 across the 62 of their papers we have counts for
82 papers
Uncertainty-Driven Replay Memory for Reinforcement Learning
Sheeraja Rajakrishnan, Alexander G. Ororbia, Travis Desell +1
Uncertainty estimation provides promising capabilities for reinforcement learning (RL) agents. Notably, estimating uncertainty can reduce the training time and enable agents to obt…
Unsupervised Continual Learning with Growing Self-Organizing Maps and Synthetic Replay
Pujan Thapa, Alexander Ororbia, Travis Desell
This work presents a generative continual learning framework based on growing self-organizing maps (GSOMs) that are augmented with learned distributional statistics as well as enco…
Error Highways: Scaling Predictive Coding to Very Deep Networks
Amirhossein Mohammadi, Alexander G. Ororbia
Predictive coding networks (PCNs) offer a biologically-plausible, local-learning alternative to back-propagation of errors (backprop). Nevertheless, they have remained largely conf…
Intrinsic Vicarious Conditioning for Deep Reinforcement Learning
Rodney A Sanchez, Ferat Sahin, Alex Ororbia +1
Advancements in reinforcement learning have produced a variety of complex and useful intrinsic driving forces; crucially, these drivers operate under a direct conditioning paradigm…
NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence
Anthony Zador, Jean-Marc Fellous, Terrence Sejnowski +28
Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected. Based on a workshop convened by the National Sci…
Optimizing Neurorobot Policy under Limited Demonstration Data through Preference Regret
Viet Dung Nguyen, Yuhang Song, Anh Nguyen +3
Robot reinforcement learning from demonstrations (RLfD) assumes that expert data is abundant; this is usually unrealistic in the real world given data scarcity as well as high coll…