DeepAI

Bipin Rajendran

is this you? claim profile

0 followers

∙

New Jersey Institute of Technology

∙

King's College London

Featured Co-authors

Osvaldo Simeone
139 publications
Abu Sebastian
22 publications
Geethan Karunaratne
14 publications
Alireza Bagheri
12 publications
Jae-sun Seo
12 publications
Evangelos Eleftheriou
11 publications
Irem Boybat
11 publications
Eleni Vasilaki
11 publications
Manuel Le Gallo
7 publications
Anakha V Babu
3 publications
Bleema Rosenfeld
3 publications

∙ 11/02/2021

Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning

Neuromorphic data carries information in spatio-temporal patterns encode...

Bleema Rosenfeld, et al. ∙

∙ 02/21/2021

Fast On-Device Adaptation for Spiking Neural Networks via Online-Within-Online Meta-Learning

Spiking Neural Networks (SNNs) have recently gained popularity as machin...

Bleema Rosenfeld, et al. ∙

∙ 02/10/2021

Hybrid In-memory Computing Architecture for the Training of Deep Neural Networks

The cost involved in training deep neural networks (DNNs) on von-Neumann...

Vinay Joshi, et al. ∙

∙ 08/05/2020

SpinAPS: A High-Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks

We discuss a high-performance and high-throughput hardware accelerator f...

Anakha V Babu, et al. ∙

∙ 04/30/2020

Memristors – from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing

Machine learning, particularly in the form of deep learning, has driven ...

Adnan Mehonic, et al. ∙

∙ 03/25/2020

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning

Deep neural networks (DNNs) have surpassed human-level accuracy in a var...

Vinay Joshi, et al. ∙

∙ 05/28/2019

Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Spiking neural networks (SNN) are artificial computational models that h...

S. R. Nandakumar, et al. ∙

∙ 01/11/2019

Low-Power Neuromorphic Hardware for Signal Processing Applications

Machine learning has emerged as the dominant tool for implementing compl...

Bipin Rajendran, et al. ∙

∙ 10/23/2018

Training Multilayer Spiking Neural Networks using NormAD based Spatio-Temporal Error Backpropagation

Spiking neural networks (SNNs) have garnered a great amount of interest ...

Navin Anwani, et al. ∙

∙ 10/23/2018

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

Artificial Neural Networks (ANNs) are currently being used as function a...

Bleema Rosenfeld, et al. ∙

∙ 02/22/2018

Adversarial Training for Probabilistic Spiking Neural Networks

Classifiers trained using conventional empirical risk minimization or ma...

Alireza Bagheri, et al. ∙

∙ 11/09/2017

Stochastic Deep Learning in Memristive Networks

We study the performance of stochastically trained deep neural networks ...

Anakha V Babu, et al. ∙

∙ 11/09/2017

Learning and Real-time Classification of Hand-written Digits With Spiking Neural Networks

We describe a novel spiking neural network (SNN) for automated, real-tim...

Shruti R. Kulkarni, et al. ∙

∙ 10/29/2017

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

Third-generation neural networks, or Spiking Neural Networks (SNNs), aim...

Alireza Bagheri, et al. ∙