53 citations · 149 across the 20 of their papers we have counts for
4 papers · 1 filter
Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference
Rishav Mukherji, Mark Schöne, Khaleelulla Khan Nazeer +2
Artificial neural networks open up unprecedented machine learning capabilities at the cost of ever growing computational requirements. Sparsifying the parameters, often achieved th…
Beyond Weights: Deep learning in Spiking Neural Networks with pure synaptic-delay training
Edoardo W. Grappolini, Anand Subramoney
Biological evidence suggests that adaptation of synaptic delays on short to medium timescales plays an important role in learning in the brain. Inspired by biology, we explore the…
Block-local learning with probabilistic latent representations
David Kappel, Khaleelulla Khan Nazeer, Cabrel Teguemne Fokam +2
The ubiquitous backpropagation algorithm requires sequential updates through the network introducing a locking problem. In addition, back-propagation relies on the transpose of for…
Efficient Real Time Recurrent Learning through combined activity and parameter sparsity
Anand Subramoney
Backpropagation through time (BPTT) is the standard algorithm for training recurrent neural networks (RNNs), which requires separate simulation phases for the forward and backward…