Priyadarshini (Priya) Panda
Priyadarshini (Priya) Panda
Assistant Professor, Electrical Engineering, Yale University
Verified email at - Homepage
Cited by
Cited by
Towards spike-based machine intelligence with neuromorphic computing
K Roy, A Jaiswal, P Panda
Nature 575 (7784), 607-617, 2019
Enabling spike-based backpropagation for training deep neural network architectures
C Lee, SS Sarwar, P Panda, G Srinivasan, K Roy
Frontiers in neuroscience, 119, 2020
Tree-CNN: A hierarchical deep convolutional neural network for incremental learning
D Roy, P Panda, K Roy
Neural Networks 121, 148-160, 2019
Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation
N Rathi, G Srinivasan, P Panda, K Roy
arXiv preprint arXiv:2005.01807, 2020
Magnetic tunnel junction mimics stochastic cortical spiking neurons
A Sengupta, P Panda, P Wijesinghe, Y Kim, K Roy
Scientific reports 6 (1), 30039, 2016
Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition
P Panda, A Sengupta, K Roy
2016 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp …, 2015
Training deep spiking convolutional neural networks with stdp-based unsupervised pre-training followed by supervised fine-tuning
C Lee, P Panda, G Srinivasan, K Roy
Frontiers in neuroscience 12, 435, 2018
Unsupervised Regenerative Learning of Hierarchical Features in Spiking Deep Networks for Object Recognition
P Panda, K Roy
2016 International Joint Conference on Neural Networks (IJCNN), pp. 299-306, 2016
Deep spiking convolutional neural network trained with unsupervised spike-timing-dependent plasticity
C Lee, G Srinivasan, P Panda, K Roy
IEEE Transactions on Cognitive and Developmental Systems 11 (3), 384-394, 2018
Gabor filter assisted energy efficient fast learning convolutional neural networks
SS Sarwar, P Panda, K Roy
2017 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2017
2022 roadmap on neuromorphic computing and engineering
DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ...
Neuromorphic Computing and Engineering 2 (2), 022501, 2022
Resparc: A reconfigurable and energy-efficient architecture with memristive crossbars for deep spiking neural networks
A Ankit, A Sengupta, P Panda, K Roy
Proceedings of the 54th Annual Design Automation Conference 2017, 1-6, 2017
Domain adaptation without source data
Y Kim, D Cho, K Han, P Panda, S Hong
IEEE Transactions on Artificial Intelligence 2 (6), 508-518, 2021
STDP-based pruning of connections and weight quantization in spiking neural networks for energy-efficient recognition
N Rathi, P Panda, K Roy
IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2018
Habituation based synaptic plasticity and organismic learning in a quantum perovskite
F Zuo, P Panda, M Kotiuga, J Li, M Kang, C Mazzoli, H Zhou, A Barbour, ...
Nature communications 8 (1), 240, 2017
Revisiting batch normalization for training low-latency deep spiking neural networks from scratch
Y Kim, P Panda
Frontiers in neuroscience, 1638, 2021
Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connections, stochastic softmax, and hybridization
P Panda, SA Aketi, K Roy
Frontiers in Neuroscience 14, 653, 2020
A low effort approach to structured CNN design using PCA
I Garg, P Panda, K Roy
IEEE Access 8, 1347-1360, 2019
STDP-based unsupervised feature learning using convolution-over-time in spiking neural networks for energy-efficient neuromorphic computing
G Srinivasan, P Panda, K Roy
ACM Journal on Emerging Technologies in Computing Systems (JETC) 14 (4), 44, 2018
Discretization based solutions for secure machine learning against adversarial attacks
P Panda, I Chakraborty, K Roy
IEEE Access 7, 70157-70168, 2019
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