Marco Canini

Artificial Intelligence · 2012–2020

A researcher in the Science corpus: 5 papers; trusts 18 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 99 citations into it; #34,687 of 35,345 by trust here, #19,873 by citations
• Optimization & Training — 70 citations into it
• Computer Vision and Pattern Recognition — 51 citations into it
• Computer Networks and Communications — 39 citations into it
• ML Systems & Hardware — 27 citations into it
• Computational Mechanics — 24 citations into it
• Electrical and Electronic Engineering — 20 citations into it
• Hardware and Architecture — 18 citations into it

Whom Marco Canini trusts overall

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

1. Willem Visser
2. Kaiming He
3. Shai Shalev-Shwartz
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Geoffrey E. Hinton
7. Dan Alistarh
8. Yoshua Bengio
9. Wujie Wen
10. Karen Simonyan

Who trusts Marco Canini overall

1. Sandra Scott-Hayward
2. Haopei Wang
3. Danda B. Rawat
4. Seungwon Shin
5. Phillip A. Porras
6. Yosr Jarraya
7. Fei Hu
8. Lei Xu
9. Guofei Gu
10. S. Reddy

Papers from Marco Canini's perspective

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

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

• Natural Compression for Distributed Deep Learning (2019) — 183 citations
• Efficient sparse collective communication and its application to accelerate distributed deep learning (2020) — 142 citations
• Scaling Distributed Machine Learning with In-Network Aggregation (2019) — 553 citations
• Sonata: query-driven streaming network telemetry (2018) — 363 citations
• A NICE Way to Test OpenFlow Applications (2012) — 583 citations