Massimo Quadrana

Information Systems · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 83 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 56 citations into it; #1,228 of 8,771 by trust here, #1,542 by citations
• Artificial Intelligence — 47 citations into it; #8,260 of 35,345 by trust here, #10,667 by citations
• Computer Vision and Pattern Recognition — 24 citations into it; #6,771 of 21,575 by trust here, #8,409 by citations
• Management Science and Operations Research — 22 citations into it; #6,937 of 11,946 by trust here, #7,638 by citations
• Recommender Systems — 17 citations into it
• Model Architectures — 15 citations into it
• Natural Language Processing — 13 citations into it
• Signal Processing — 12 citations into it

Whom Massimo Quadrana trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Tomas Mikolov
4. Balázs Hidasi
5. Ilya Sutskever
6. Alex Krizhevsky
7. Alex Graves
8. Ruslan Salakhutdinov
9. Kyunghyun Cho
10. Zoubin Ghahramani

Who trusts Massimo Quadrana overall

1. Malte Ludewig
2. Dietmar Jannach
3. Shoujin Wang
4. Aminu Da’u
5. Kun Zhou
6. Xiaowen Huang
7. Paolo Cremonesi
8. Jin Huang
9. Wayne Xin Zhao
10. Maurizio Ferrari Dacrema

Papers from Massimo Quadrana's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Going deeper with convolutions
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Sequence-Aware Recommender Systems (2018) — 565 citations
• Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks (2017) — 711 citations
• Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations (2016) — 544 citations