918 citations · 2.5k across the 22 of their papers we have counts for
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Adversarial Training for Probabilistic Spiking Neural Networks
Alireza Bagheri, Osvaldo Simeone, Bipin Rajendran
Classifiers trained using conventional empirical risk minimization or maximum likelihood methods are known to suffer dramatic performance degradations when tested over examples adv…
Stochastic Deep Learning in Memristive Networks
Anakha V Babu, Bipin Rajendran
We study the performance of stochastically trained deep neural networks (DNNs) whose synaptic weights are implemented using emerging memristive devices that exhibit limited dynamic…
Learning and Real-time Classification of Hand-written Digits With Spiking Neural Networks
Shruti R. Kulkarni, John M. Alexiades, Bipin Rajendran
We describe a novel spiking neural network (SNN) for automated, real-time handwritten digit classification and its implementation on a GP-GPU platform. Information processing withi…
Training Probabilistic Spiking Neural Networks with First-to-spike Decoding
Alireza Bagheri, Osvaldo Simeone, Bipin Rajendran
Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that opera…