Diego Marcheggiani

Artificial Intelligence · 2010–2018

A researcher in the Science corpus: 7 papers; trusts 424 researchers and is trusted by 118 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 175 citations into it; #2,210 of 35,345 by trust here, #4,722 by citations
• Natural Language Processing — 121 citations into it; #765 of 5,688 by trust here, #1,779 by citations
• Computer Vision and Pattern Recognition — 53 citations into it
• Model Architectures — 28 citations into it
• Representation & Self-Supervised Learning — 26 citations into it; #2,900 of 3,145 by trust here, #3,127 by citations
• Evaluation & Benchmarks — 18 citations into it
• Graph Learning — 17 citations into it; #62 of 693 by trust here, #149 by citations
• Molecular Biology — 12 citations into it; #501 of 17,773 by trust here, #6,960 by citations

Whom Diego Marcheggiani trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Graves
6. Kaiming He
7. Yann LeCun
8. Alex Krizhevsky
9. Richard Socher
10. Christopher D. Manning

Who trusts Diego Marcheggiani overall

1. Marco Damonte
2. William L. Hamilton
3. Linfeng Song
4. Mihir Kale
5. Junlin Yang
6. Xavier Bresson
7. Qian Li
8. Ivan Titov
9. Chaitanya K. Joshi
10. Thomas Laurent

Papers from Diego Marcheggiani's perspective

Ordered by how strongly the researchers Diego Marcheggiani 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling (2017) — 111 citations
• Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling (2017) — 885 citations
• Graph Convolutional Encoders for Syntax-aware Neural Machine Translation (2017) — 519 citations
• Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations (2016) — 83 citations
• Semantic Role Labeling (2010) — 305 citations
• Deep Graph Convolutional Encoders for Structured Data to Text Generation (2018) — 127 citations
• Word Embeddings Go to Italy: A Comparison of Models and Training Datasets (2015) — 52 citations