Alessio Zappone
Electrical and Electronic Engineering · 2015–2020
A researcher in the Science corpus: 11 papers; trusts 140 researchers and is trusted by 29 researchers here, as inferred from the citation graph.
Subfields
- Electrical and Electronic Engineering — 329 citations into it; #994 of 16,539 by trust here, #2,466 by citations
- Aerospace Engineering — 168 citations into it; #2,184 of 6,421 by trust here, #551 by citations
- Computer Networks and Communications — 136 citations into it; #2,232 of 9,886 by trust here, #1,672 by citations
- Artificial Intelligence — 55 citations into it; #35,314 of 35,345 by trust here, #20,836 by citations
- Electronic, Optical and Magnetic Materials — 47 citations into it
- Optimization & Training — 28 citations into it
- Atomic and Molecular Physics, and Optics — 18 citations into it
- Computer Vision and Pattern Recognition — 18 citations into it
Whom Alessio Zappone trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Diederik P. Kingma
- Ronald J. Williams
- Alex Krizhevsky
- Yann LeCun
- Jeffrey G. Andrews
- Yurii E. Nesterov
- Ilya Sutskever
- Radford M. Neal
Who trusts Alessio Zappone overall
- M. Di Renzo
- Chongwen Huang
- Emil Björnson
- Yuanwei Liu
- Cunhua Pan
- Merouane Debbah
- Qurrat-Ul-Ain Nadeem
- Xidong Mu
- Kaiming Shen
- Qingqing Wu
Papers from Alessio Zappone's perspective
Ordered by how strongly the researchers Alessio Zappone trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- GENERATIVE ADVERSARIAL NETS
Their papers
- Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come (2019) — 1,115 citations
- Holographic MIMO Surfaces for 6G Wireless Networks: Opportunities, Challenges, and Trends (2019) — 1,237 citations
- Energy Efficient Multi-User MISO Communication Using Low Resolution Large Intelligent Surfaces (2018) — 309 citations
- Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead (2020) — 1,415 citations
- Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both? (2019) — 563 citations
- Energy Efficiency in Wireless Networks via Fractional Programming Theory (2015) — 637 citations
- A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead (2016) — 694 citations
- User-Centric 5G Cellular Networks: Resource Allocation and Comparison With the Cell-Free Massive MIMO Approach (2018) — 328 citations
- Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems (2019) — 71 citations
- Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead (2020) — 2,293 citations
- Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication (2018) — 2,870 citations