Anastasios Tefas

Computer Vision and Pattern Recognition · 2006–2022

A researcher in the Science corpus: 11 papers; trusts 834 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 148 citations into it; #4,794 of 21,575 by trust here, #9,563 by citations
• Artificial Intelligence — 114 citations into it; #6,104 of 35,345 by trust here, #14,438 by citations
• Efficiency & Compression — 41 citations into it
• Representation & Self-Supervised Learning — 36 citations into it
• Model Architectures — 27 citations into it
• Signal Processing — 20 citations into it; #3,831 of 7,572 by trust here, #4,201 by citations
• Optimization & Training — 19 citations into it
• Management Science and Operations Research — 14 citations into it; #5,596 of 11,946 by trust here, #6,314 by citations

Whom Anastasios Tefas 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. Ross B. Girshick
10. Sergey Ioffe

Who trusts Anastasios Tefas overall

1. Nikolaos Passalis
2. Jianping Gou
3. Alexandros Iosifidis
4. Dat Thanh Tran
5. Weiwei Jiang
6. Maria Tzelepi
7. A. Ozbayoglu
8. Omer Berat Sezer
9. Baosheng Yu
10. Rafael Müller

Papers from Anastasios Tefas's perspective

Ordered by how strongly the researchers Anastasios Tefas 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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
• Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification (2006) — 354 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
• Discriminant Bag of Words based representation for human action recognition (2014) — 80 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
• Minimum Variance Extreme Learning Machine for human action recognition (2014) — 34 citations
• View-Invariant Action Recognition Based on Artificial Neural Networks (2012) — 143 citations