Shree Krishna Sharma

Electrical and Electronic Engineering · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 53 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 117 citations into it; #5,766 of 16,539 by trust here, #9,631 by citations
• Computer Networks and Communications — 67 citations into it; #4,428 of 9,886 by trust here, #8,543 by citations
• Artificial Intelligence — 56 citations into it
• Aerospace Engineering — 18 citations into it
• Biomedical Engineering — 13 citations into it
• Signal Processing — 13 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Atomic and Molecular Physics, and Optics — 10 citations into it

Whom Shree Krishna Sharma trusts overall

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

1. Paul J. Werbos
2. Richard S. Sutton
3. M. Mohammadi
4. Yoshua Bengio
5. Christopher J. C. H. Watkins
6. Dapeng Oliver Wu
7. L. David
8. Andrew Barto
9. Ala Al-Fuqaha
10. Lucian Busoniu

Who trusts Shree Krishna Sharma overall

1. Oyeniyi Akeem Alimi
2. Fengxian Guo
3. Khmaies Ouahada
4. F. Yu
5. Adnan M. Abu-Mahfouz
6. Heli Zhang

Papers from Shree Krishna Sharma's perspective

Ordered by how strongly the researchers Shree Krishna Sharma 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. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019) — 490 citations
• Simultaneous Wireless Information and Power Transfer (SWIPT): Recent Advances and Future Challenges (2018) — 792 citations
• Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions (2018) — 356 citations