Sumudu Samarakoon
Artificial Intelligence · 2017–2020
A researcher in the Science corpus: 8 papers; trusts 80 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 186 citations into it; #8,611 of 35,345 by trust here, #5,924 by citations
- Electrical and Electronic Engineering — 156 citations into it; #6,277 of 16,539 by trust here, #4,677 by citations
- Computer Networks and Communications — 117 citations into it; #2,569 of 9,886 by trust here, #1,475 by citations
- Computer Vision and Pattern Recognition — 56 citations into it
- Optimization & Training — 53 citations into it
- Federated & Distributed Learning — 44 citations into it; #191 of 319 by trust here, #102 by citations
- Efficiency & Compression — 32 citations into it
- Aerospace Engineering — 26 citations into it
Whom Sumudu Samarakoon trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Medhi Bennis
- Jihong Park
- Geoffrey E. Hinton
- Chen-Feng Liu
- Alex Krizhevsky
- Yoshua Bengio
- Walid Saad
- Iroon Polytechniou
- Yann LeCun
- Diederik P. Kingma
Who trusts Sumudu Samarakoon overall
- Jihong Park
- Medhi Bennis
- Mingzhe Chen
- Zhaohui Yang
- Anis Elgabli
- Khaled Ben Letaief
- Lixu Wang
- Boyi Liu
- Changyang She
- H. Vincent Poor
Papers from Sumudu Samarakoon's perspective
Ordered by how strongly the researchers Sumudu Samarakoon trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Federated Learning for Ultra-Reliable Low-Latency V2V Communications (2018) — 243 citations
- Wireless Network Intelligence at the Edge (2018) — 588 citations
- Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications (2018) — 420 citations
- Vehicle clustering for improving enhanced LTE-V2X network performance (2017) — 46 citations
- Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail (2018) — 57 citations
- Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications (2020) — 185 citations
- Extreme URLLC: Vision, Challenges, and Key Enablers (2020) — 109 citations
- Wireless Edge Computing With Latency and Reliability Guarantees (2019) — 149 citations