Lucas Drumond

Artificial Intelligence · 2010–2017

A researcher in the Science corpus: 3 papers; trusts 15 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 21 citations into it; #32,247 of 35,345 by trust here, #18,186 by citations
• Information Systems — 21 citations into it; #3,801 of 8,771 by trust here, #2,741 by citations
• Computer Vision and Pattern Recognition — 13 citations into it; #19,828 of 21,575 by trust here, #19,310 by citations
• Recommender Systems — 9 citations into it; #152 of 233 by trust here, #204 by citations
• Statistical and Nonlinear Physics — 6 citations into it; #5,437 of 10,127 by trust here, #7,402 by citations
• Computational Mathematics — 5 citations into it

Whom Lucas Drumond trusts overall

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

1. Steffen Rendle
2. Ajit Paul Singh
3. Lei Tang
4. Mark Newman
5. Zeno Gantner
6. Arkadiusz Paterek
7. Yehuda Koren
8. Lars Schmidt-Thieme
9. Christoph Freudenthaler
10. Deepak K. Agarwal

Who trusts Lucas Drumond overall

1. Ruining He
2. Julian McAuley
3. Qing Zhang
4. Xiangyu Zhao
5. Hao Wang
6. Xia Ning
7. Trapit Bansal
8. Immanuel Bayer

Papers from Lucas Drumond's perspective

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

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

Their papers

• Personalized Deep Learning for Tag Recommendation (2017) — 66 citations
• Multi-relational matrix factorization using bayesian personalized ranking for social network data (2012) — 201 citations
• Learning Attribute-to-Feature Mappings for Cold-Start Recommendations (2010) — 322 citations