149 citations · 154 across the 4 of their papers we have counts for
4 papers
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor
Kenneth Stewart, Garrick Orchard, Sumit Bam Shrestha +1
Recent work suggests that synaptic plasticity dynamics in biological models of neurons and neuromorphic hardware are compatible with gradient-based learning (Neftci et al., 2019).…
Inherent Weight Normalization in Stochastic Neural Networks
Georgios Detorakis, Sourav Dutta, Abhishek Khanna +3
Multiplicative stochasticity such as Dropout improves the robustness and generalizability of deep neural networks. Here, we further demonstrate that always-on multiplicative stocha…
Surrogate Gradient Learning in Spiking Neural Networks
Emre O. Neftci, Hesham Mostafa, Friedemann Zenke
Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signal processing. To translate these benefits into hardware, a growing number of neu…
Learning Non-deterministic Representations with Energy-based Ensembles
Maruan Al-Shedivat, Emre Neftci, Gert Cauwenberghs
The goal of a generative model is to capture the distribution underlying the data, typically through latent variables. After training, these variables are often used as a new repre…