Elena Mocanu

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 5 papers; trusts 33 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #26,522 of 35,345 by trust here, #13,353 by citations
• Computer Vision and Pattern Recognition — 37 citations into it
• Probabilistic & Bayesian Methods — 22 citations into it
• Statistical and Nonlinear Physics — 15 citations into it
• Model Architectures — 10 citations into it; #1,922 of 2,278 by trust here, #1,352 by citations
• Representation & Self-Supervised Learning — 9 citations into it
• Optimization & Training — 6 citations into it; #2,358 of 2,601 by trust here, #1,349 by citations

Whom Elena Mocanu trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ruslan Salakhutdinov
4. Paul Smolensky
5. Alex Krizhevsky
6. Kaiming He
7. Hugo Larochelle
8. Tijmen Tieleman
9. Yann LeCun
10. Radford M. Neal

Who trusts Elena Mocanu overall

1. Decebal Constantin Mocanu
2. Kasun Amarasinghe
3. Milos Manic
4. Daniel L. Marino
5. T. Kim
6. Alberto Gasparin
7. M. Mohammadi
8. Aowabin Rahman
9. Seunghyoung Ryu
10. A. Perera

Papers from Elena Mocanu's perspective

Ordered by how strongly the researchers Elena Mocanu 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science (2017) — 750 citations
• On-Line Building Energy Optimization Using Deep Reinforcement Learning (2017) — 552 citations
• Deep learning for estimating building energy consumption (2016) — 587 citations
• Big IoT data mining for real-time energy disaggregation in buildings (2016) — 42 citations
• A topological insight into restricted Boltzmann machines (2016) — 102 citations