Nikolaos Passalis

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 7 papers; trusts 605 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it; #7,177 of 35,345 by trust here, #16,771 by citations
• Computer Vision and Pattern Recognition — 88 citations into it; #5,350 of 21,575 by trust here, #11,721 by citations
• Efficiency & Compression — 41 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Model Architectures — 25 citations into it
• Optimization & Training — 18 citations into it
• Management Science and Operations Research — 14 citations into it; #5,598 of 11,946 by trust here, #6,314 by citations
• Signal Processing — 14 citations into it; #3,833 of 7,572 by trust here, #4,201 by citations

Whom Nikolaos Passalis trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Yoshua Bengio
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. Jimmy Ba
9. Sergey Ioffe
10. Christian Szegedy

Who trusts Nikolaos Passalis overall

1. Jianping Gou
2. Baosheng Yu
3. Weiwei Jiang
4. Maria Tzelepi
5. Rafael Müller
6. Shuxuan Guo
7. Dat Thanh Tran
8. Guodong Xu
9. Qiankun Gao
10. Ancong Wu

Papers from Nikolaos Passalis's perspective

Ordered by how strongly the researchers Nikolaos Passalis 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning Deep Representations with Probabilistic Knowledge Transfer (2018) — 487 citations
• Using deep learning to detect price change indications in financial markets (2017) — 145 citations
• Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks (2017) — 329 citations
• Knowledge distillation (2022)
• Probabilistic Knowledge Transfer for Lightweight Deep Representation Learning (2020) — 135 citations
• Heterogeneous Knowledge Distillation Using Information Flow Modeling (2020) — 175 citations
• Time-series classification using neural Bag-of-Features (2017) — 39 citations