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Priyadarshini (Priya) Panda
Priyadarshini (Priya) Panda
Assistant Professor, Electrical Engineering, Yale University
在 yale.edu 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Towards spike-based machine intelligence with neuromorphic computing
K Roy, A Jaiswal, P Panda
Nature 575 (7784), 607-617, 2019
13662019
Enabling spike-based backpropagation for training deep neural network architectures
C Lee, SS Sarwar, P Panda, G Srinivasan, K Roy
Frontiers in neuroscience 14, 497482, 2020
3972020
2022 roadmap on neuromorphic computing and engineering
DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ...
Neuromorphic Computing and Engineering 2 (2), 022501, 2022
3582022
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
3092020
Tree-CNN: A hierarchical deep convolutional neural network for incremental learning
D Roy, P Panda, K Roy
Neural Networks 121, 148-160, 2019
2802019
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, 373945, 2018
2152018
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
2042015
Magnetic tunnel junction mimics stochastic cortical spiking neurons
A Sengupta, P Panda, P Wijesinghe, Y Kim, K Roy
Scientific reports 6 (1), 30039, 2016
2022016
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
187*2021
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
1492018
Revisiting batch normalization for training low-latency deep spiking neural networks from scratch
Y Kim, P Panda
Frontiers in neuroscience 15, 773954, 2021
1472021
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
1352016
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
1292017
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
1272018
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
1162017
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, 535502, 2020
1112020
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
1062017
Neural architecture search for spiking neural networks
Y Kim, Y Li, H Park, Y Venkatesha, P Panda
ECCV 2022, 2022
862022
Optimizing deeper spiking neural networks for dynamic vision sensing
Y Kim, P Panda
Neural Networks 144, 686-698, 2021
852021
Inherent adversarial robustness of deep spiking neural networks: Effects of discrete input encoding and non-linear activations
S Sharmin, N Rathi, P Panda, K Roy
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
832020
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