Paolo Cremonesi

Information Systems · 2010–2019

A researcher in the Science corpus: 6 papers; trusts 86 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 98 citations into it; #455 of 8,771 by trust here, #589 by citations
• Artificial Intelligence — 63 citations into it; #14,042 of 35,345 by trust here, #13,760 by citations
• Management Science and Operations Research — 36 citations into it; #1,066 of 11,946 by trust here, #857 by citations
• Computer Vision and Pattern Recognition — 30 citations into it; #11,023 of 21,575 by trust here, #9,305 by citations
• Recommender Systems — 28 citations into it
• Signal Processing — 18 citations into it
• Marketing — 14 citations into it; #503 of 6,289 by trust here, #1,168 by citations
• Computer Networks and Communications — 10 citations into it

Whom Paolo Cremonesi trusts overall

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

1. Yehuda Koren
2. Yoshua Bengio
3. George Karypis
4. Geoffrey E. Hinton
5. Tomas Mikolov
6. Badrul Munir Sarwar
7. Ilya Sutskever
8. Zoubin Ghahramani
9. Alex Graves
10. Diederik P. Kingma

Who trusts Paolo Cremonesi overall

1. Harald Steck
2. Anshumali Shrivastava
3. Xiangnan He
4. Yongfeng Zhang
5. Suvash Sedhain
6. Xia Ning
7. Hongzhi Yin
8. Ping Li
9. Yue Shi
10. Behnam Neyshabur

Papers from Paolo Cremonesi's perspective

Ordered by how strongly the researchers Paolo Cremonesi 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. Learning Multiple Layers of Features from Tiny Images
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Are we really making much progress? A worrying analysis of recent neural recommendation approaches (2019) — 686 citations
• Performance of recommender algorithms on top-n recommendation tasks (2010) — 1,543 citations
• Sequence-Aware Recommender Systems (2018) — 565 citations
• Cross-Domain Recommender Systems (2015) — 326 citations
• Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks (2017) — 711 citations
• Hybrid algorithms for recommending new items (2011) — 46 citations