8 citations · 14 across the 5 of their papers we have counts for
5 papers
Understanding and Improving Optimization in Predictive Coding Networks
Nick Alonso, Jeff Krichmar, Emre Neftci
Backpropagation (BP), the standard learning algorithm for artificial neural networks, is often considered biologically implausible. In contrast, the standard learning algorithm for…
A Theoretical Framework for Inference Learning
Nick Alonso, Beren Millidge, Jeff Krichmar +1
Backpropagation (BP) is the most successful and widely used algorithm in deep learning. However, the computations required by BP are challenging to reconcile with known neurobiolog…
Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability
Jinwei Xing, Takashi Nagata, Xinyun Zou +2
Although deep Reinforcement Learning (RL) has proven successful in a wide range of tasks, one challenge it faces is interpretability when applied to real-world problems. Saliency m…
Tightening the Biological Constraints on Gradient-Based Predictive Coding
Nick Alonso, Emre Neftci
Predictive coding (PC) is a general theory of cortical function. The local, gradient-based learning rules found in one kind of PC model have recently been shown to closely approxim…
Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation
Jinwei Xing, Takashi Nagata, Kexin Chen +3
Despite the recent success of deep reinforcement learning (RL), domain adaptation remains an open problem. Although the generalization ability of RL agents is critical for the real…