2 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
Training a Predictive Coding Network on ImageNet using Equilibrium Propagation
Tugdual Kerjan, Rasmus Høier, Benjamin Scellier
Equilibrium Propagation (EP) is a physics-based training framework that has primarily been employed in energy-based models, including continuous Hopfield networks, nonlinear resist…
Two Tales of Single-Phase Contrastive Hebbian Learning
Rasmus Kjær Høier, Christopher Zach
The search for ``biologically plausible'' learning algorithms has converged on the idea of representing gradients as activity differences. However, most approaches require a high d…
Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons
Rasmus Høier, D. Staudt, Christopher Zach
Activity difference based learning algorithms-such as contrastive Hebbian learning and equilibrium propagation-have been proposed as biologically plausible alternatives to error ba…
AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks
Huu Le, Rasmus Kjær Høier, Che-Tsung Lin +1
We propose a new algorithm for training deep neural networks (DNNs) with binary weights. In particular, we first cast the problem of training binary neural networks (BiNNs) as a bi…
Lifted Regression/Reconstruction Networks
Rasmus Kjær Høier, Christopher Zach
In this work we propose lifted regression/reconstruction networks (LRRNs), which combine lifted neural networks with a guaranteed Lipschitz continuity property for the output layer…