Sotirios P. Chatzis

Artificial Intelligence · 2012–2018

A researcher in the Science corpus: 7 papers; trusts 154 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #9,099 of 35,345 by trust here, #22,463 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Information Systems — 23 citations into it; #1,811 of 8,771 by trust here, #6,195 by citations
• Management Science and Operations Research — 17 citations into it; #2,664 of 11,946 by trust here, #6,563 by citations
• Probabilistic & Bayesian Methods — 16 citations into it
• Optimization & Training — 12 citations into it
• Recommender Systems — 12 citations into it
• Signal Processing — 11 citations into it; #4,557 of 7,572 by trust here, #7,112 by citations

Whom Sotirios P. Chatzis trusts overall

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

1. Radford M. Neal
2. Ruslan Salakhutdinov
3. Diederik P. Kingma
4. Michael I. Jordan
5. Yoshua Bengio
6. Geoffrey E. Hinton
7. Yee Whye Teh
8. Zoubin Ghahramani
9. Alex Krizhevsky
10. Thomas Hofmann

Who trusts Sotirios P. Chatzis overall

1. Sheng Li
2. Xinbo Jiang
3. Xin Luo
4. Panayiotis Christodoulou
5. Jaya Kawale
6. Zeynep Batmaz
7. Dawen Liang
8. Fan Zhong
9. A. Ozbayoglu
10. Ye Yuan

Papers from Sotirios P. Chatzis's perspective

Ordered by how strongly the researchers Sotirios P. Chatzis 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. Going deeper with convolutions

Their papers

• Recurrent Latent Variable Networks for Session-Based Recommendation (2017) — 43 citations
• Nonparametric bayesian multitask collaborative filtering (2013) — 29 citations
• Forecasting stock market crisis events using deep and statistical machine learning techniques (2018) — 276 citations
• Nonparametric Bayesian Deep Networks with Local Competition (2018) — 32 citations
• A conditional random field-based model for joint sequence segmentation and classification (2013) — 23 citations
• Infinite Markov-Switching Maximum Entropy Discrimination Machines (2013) — 8 citations
• A Coupled Indian Buffet Process Model for Collaborative Filtering (2012) — 10 citations