Flavian Vasile

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 76 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #9,946 of 35,345 by trust here, #19,873 by citations
• Information Systems — 31 citations into it; #1,593 of 8,771 by trust here, #4,012 by citations
• Computer Vision and Pattern Recognition — 15 citations into it
• Management Science and Operations Research — 15 citations into it; #3,380 of 11,946 by trust here, #6,888 by citations
• Recommender Systems — 15 citations into it; #127 of 233 by trust here, #208 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Natural Language Processing — 12 citations into it
• Model Architectures — 9 citations into it; #1,205 of 2,278 by trust here, #2,128 by citations

Whom Flavian Vasile trusts overall

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

1. Tomas Mikolov
2. Yoshua Bengio
3. Ilya Sutskever
4. Geoffrey E. Hinton
5. Ruslan Salakhutdinov
6. Yehuda Koren
7. Quoc V. Le
8. Christopher C. Johnson
9. Thorsten Joachims
10. Andriy Mnih

Who trusts Flavian Vasile overall

1. Elena Smirnova
2. Massimo Quadrana
3. Shoujin Wang
4. Huan Zhao
5. Dietmar Jannach
6. Xiangnan He
7. Paolo Cremonesi
8. Dawen Liang
9. Le Wu
10. Fajie Yuan

Papers from Flavian Vasile's perspective

Ordered by how strongly the researchers Flavian Vasile 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. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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

• Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks (2017) — 173 citations
• Causal embeddings for recommendation (2017) — 317 citations
• Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation (2016) — 273 citations