217 citations · 424 across the 13 of their papers we have counts for
5 papers · 1 filter
Deep supervised learning using local errors
Hesham Mostafa, Vishwajith Ramesh, Gert Cauwenberghs
Error backpropagation is a highly effective mechanism for learning high-quality hierarchical features in deep networks. Updating the features or weights in one layer, however, requ…
Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
Georgios Detorakis, Sadique Sheik, Charles Augustine +6
Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware. However, neuromorphic implementations of embedded learning at large…
Hardware-efficient on-line learning through pipelined truncated-error backpropagation in binary-state networks
Hesham Mostafa, Bruno Pedroni, Sadique Sheik +1
Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significa…
A learning framework for winner-take-all networks with stochastic synapses
Hesham Mostafa, Gert Cauwenberghs
Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the d…
Membrane-Dependent Neuromorphic Learning Rule for Unsupervised Spike Pattern Detection
Sadique Sheik, Somnath Paul, Charles Augustine +1
Several learning rules for synaptic plasticity, that depend on either spike timing or internal state variables, have been proposed in the past imparting varying computational capab…