121 citations · 260 across the 20 of their papers we have counts for
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Multi-Sample Online Learning for Spiking Neural Networks based on Generalized Expectation Maximization
Hyeryung Jang, Osvaldo Simeone
Spiking Neural Networks (SNNs) offer a novel computational paradigm that captures some of the efficiency of biological brains by processing through binary neural dynamic activation…
BiSNN: Training Spiking Neural Networks with Binary Weights via Bayesian Learning
Hyeryung Jang, Nicolas Skatchkovsky, Osvaldo Simeone
Artificial Neural Network (ANN)-based inference on battery-powered devices can be made more energy-efficient by restricting the synaptic weights to be binary, hence eliminating the…
Multi-Sample Online Learning for Probabilistic Spiking Neural Networks
Hyeryung Jang, Osvaldo Simeone
Spiking Neural Networks (SNNs) capture some of the efficiency of biological brains for inference and learning via the dynamic, online, event-driven processing of binary time series…
VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All Circuits
Hyeryung Jang, Nicolas Skatchkovsky, Osvaldo Simeone
Networks of spiking neurons and Winner-Take-All spiking circuits (WTA-SNNs) can detect information encoded in spatio-temporal multi-valued events. These are described by the timing…
An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications
Hyeryung Jang, Osvaldo Simeone, Brian Gardner +1
Spiking neural networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synap…
Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Spiking Neural Networks (SNNs) offer a promising alternative to conventional Artificial Neural Networks (ANNs) for the implementation of on-device low-power online learning and inf…
Solving Continual Combinatorial Selection via Deep Reinforcement Learning
Hyungseok Song, Hyeryung Jang, Hai H. Tran +5
We consider the Markov Decision Process (MDP) of selecting a subset of items at each step, termed the Select-MDP (S-MDP). The large state and action spaces of S-MDPs make them intr…
Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing
Hyeryung Jang, Osvaldo Simeone
Neuromorphic hardware platforms, such as Intel's Loihi chip, support the implementation of Spiking Neural Networks (SNNs) as an energy-efficient alternative to Artificial Neural Ne…
Adiabatic Persistent Contrastive Divergence Learning
Hyeryung Jang, Hyungwon Choi, Yung Yi +1
This paper studies the problem of parameter learning in probabilistic graphical models having latent variables, where the standard approach is the expectation maximization algorith…