Get our free extension to see links to code for papers anywhere online!

Add to Chrome

Add to Firefox

Picture for Osvaldo Simeone

Osvaldo Simeone

Spiking Neural Networks: A Stochastic Signal Processing Perspective

Dec 11, 2018
Hyeryung Jang, Osvaldo Simeone, Brian Gardner, André Grüning

Figure 1 for Spiking Neural Networks: A Stochastic Signal Processing Perspective

Figure 2 for Spiking Neural Networks: A Stochastic Signal Processing Perspective

Figure 3 for Spiking Neural Networks: A Stochastic Signal Processing Perspective

Figure 4 for Spiking Neural Networks: A Stochastic Signal Processing Perspective

* 21 pages, 6 figures

Access Paper or Ask Questions

Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients

Oct 25, 2018
Bleema Rosenfeld, Osvaldo Simeone, Bipin Rajendran

Figure 1 for Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients

Figure 2 for Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients

Figure 3 for Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients

Figure 4 for Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients

* Submitted for conference publication

Access Paper or Ask Questions

Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing

Oct 23, 2018
Hyeryung Jang, Osvaldo Simeone

Figure 1 for Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing

Figure 2 for Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing

Figure 3 for Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing

Figure 4 for Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing

* 14 pages, 6 figures

Access Paper or Ask Questions

A Brief Introduction to Machine Learning for Engineers

May 17, 2018
Osvaldo Simeone

Figure 1 for A Brief Introduction to Machine Learning for Engineers

Figure 2 for A Brief Introduction to Machine Learning for Engineers

Figure 3 for A Brief Introduction to Machine Learning for Engineers

Figure 4 for A Brief Introduction to Machine Learning for Engineers

* This is an expanded and improved version of the original posting. Feedback is welcome

Access Paper or Ask Questions

Adversarial Training for Probabilistic Spiking Neural Networks

Feb 26, 2018
Alireza Bagheri, Osvaldo Simeone, Bipin Rajendran

Figure 1 for Adversarial Training for Probabilistic Spiking Neural Networks

Figure 2 for Adversarial Training for Probabilistic Spiking Neural Networks

Figure 3 for Adversarial Training for Probabilistic Spiking Neural Networks

Figure 4 for Adversarial Training for Probabilistic Spiking Neural Networks

* Submitted for possible publication. arXiv admin note: text overlap with arXiv:1710.10704

Access Paper or Ask Questions

Training Probabilistic Spiking Neural Networks with First-to-spike Decoding

Feb 22, 2018
Alireza Bagheri, Osvaldo Simeone, Bipin Rajendran

Figure 1 for Training Probabilistic Spiking Neural Networks with First-to-spike Decoding

Figure 2 for Training Probabilistic Spiking Neural Networks with First-to-spike Decoding

Figure 3 for Training Probabilistic Spiking Neural Networks with First-to-spike Decoding

Figure 4 for Training Probabilistic Spiking Neural Networks with First-to-spike Decoding

* A shorter version will be published on Proc. IEEE ICASSP 2018

Access Paper or Ask Questions

Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network

Aug 20, 2014
Yu Liu, Osvaldo Simeone, Alexander M. Haimovich, Wei Su

Figure 1 for Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network

Figure 2 for Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network

Figure 3 for Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network

Figure 4 for Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network

* Contains corrections with respect to the version to appear on IEEE Signal Processing Letters (see Fig. 2)

Access Paper or Ask Questions