Priyadarshini Panda

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 10 papers; trusts 740 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 126 citations into it; #10,564 of 35,345 by trust here, #15,004 by citations
• Computer Vision and Pattern Recognition — 118 citations into it; #7,583 of 21,575 by trust here, #12,689 by citations
• Electrical and Electronic Engineering — 90 citations into it; #2,683 of 16,539 by trust here, #6,973 by citations
• Cognitive Neuroscience — 56 citations into it; #3,770 of 10,852 by trust here, #6,360 by citations
• Model Architectures — 43 citations into it; #1,917 of 2,278 by trust here, #1,794 by citations
• Efficiency & Compression — 34 citations into it; #1,135 of 1,264 by trust here, #1,130 by citations
• Cellular and Molecular Neuroscience — 33 citations into it; #2,767 of 2,998 by trust here, #2,131 by citations
• Optimization & Training — 33 citations into it

Whom Priyadarshini Panda trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Yann LeCun
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Kaiming He
6. Christian Szegedy
7. Ilya Sutskever
8. Karen Simonyan
9. Diederik P. Kingma
10. X. Zhang

Who trusts Priyadarshini Panda overall

1. Umberto Michieli
2. Gobinda Saha
3. Kaushik Roy
4. Simone Scardapane
5. Wei Fang
6. Chi Lo
7. Zhihang Yuan
8. Pietro Zanuttigh
9. Isha Garg
10. Man Yao

Papers from Priyadarshini Panda's perspective

Ordered by how strongly the researchers Priyadarshini Panda trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Discretization Based Solutions for Secure Machine Learning Against Adversarial Attacks (2019) — 42 citations
• Towards spike-based machine intelligence with neuromorphic computing (2019) — 2,181 citations
• Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures (2019) — 452 citations
• Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation (2020) — 356 citations
• 2022 roadmap on neuromorphic computing and engineering (2021) — 589 citations
• Tree-CNN: A Deep Convolutional Neural Network for Lifelong Learning (2018) — 29 citations
• A Low Effort Approach to Structured CNN Design Using PCA (2018) — 69 citations
• Tree-CNN: A hierarchical Deep Convolutional Neural Network for incremental learning (2018) — 242 citations
• Energy-Efficient and Improved Image Recognition with Conditional Deep Learning (2017) — 43 citations
• Conditional Deep Learning for energy-efficient and enhanced pattern recognition (2015) — 208 citations