Giovanni Neglia

Artificial Intelligence · 2006–2021

A researcher in the Science corpus: 5 papers; trusts 67 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 124 citations into it; #35,324 of 35,345 by trust here, #25,809 by citations
• Optimization & Training — 64 citations into it
• Computer Networks and Communications — 55 citations into it; #5,450 of 9,886 by trust here, #5,733 by citations
• Federated & Distributed Learning — 45 citations into it
• ML Systems & Hardware — 27 citations into it
• Computational Mechanics — 22 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Electrical and Electronic Engineering — 13 citations into it

Whom Giovanni Neglia trusts overall

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

1. Angelia Nedić
2. John C. Duchi
3. Alex Krizhevsky
4. Brendan D. McKay
5. Yann LeCun
6. Geoffrey E. Hinton
7. Yoshua Bengio
8. Kaiming He
9. Yurii E. Nesterov
10. L. Eon Bottou

Who trusts Giovanni Neglia overall

1. Othmane Marfoq
2. Khaled Ben Letaief
3. Thrasyvoulos Spyropoulos
4. Edwige Cyffers
5. Chuan Xu
6. Aurélien Bellet
7. Falko Dressler
8. Yuanming Shi
9. Konstantinos Psounis

Papers from Giovanni Neglia's perspective

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

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

• Throughput-Optimal Topology Design for Cross-Silo Federated Learning (2020) — 113 citations
• Federated Multi-Task Learning under a Mixture of Distributions (2021) — 398 citations
• Decentralized gradient methods: does topology matter? (2020) — 67 citations
• The Role of Network Topology for Distributed Machine Learning (2019) — 59 citations
• Performance modeling of epidemic routing (2006) — 912 citations