Peter Tino

Artificial Intelligence · 1995–2020

A researcher in the Science corpus: 8 papers; trusts 63 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 100 citations into it; #12,723 of 35,345 by trust here, #16,166 by citations
• Safety, Robustness & Interpretability — 55 citations into it
• Computer Vision and Pattern Recognition — 41 citations into it
• Model Architectures — 28 citations into it; #1,095 of 2,278 by trust here, #1,231 by citations
• Natural Language Processing — 11 citations into it
• Optimization & Training — 11 citations into it
• Electrical and Electronic Engineering — 8 citations into it; #12,857 of 16,539 by trust here, #10,714 by citations
• Statistics and Probability — 7 citations into it

Whom Peter Tino trusts overall

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

1. Ronald J. Williams
2. Michael Casey
3. Tim Bollerslev
4. Jeffrey L. Elman
5. Teuvo Kohonen
6. Geoffrey E. Hinton
7. Herbert Jaeger
8. Yoshua Bengio
9. J. Urgen Schmidhuber
10. Morten H. Christiansen

Who trusts Peter Tino overall

1. Henrik Jacobsson
2. Omer Berat Sezer
3. A. Ozbayoglu
4. M. Ugur Gudelek
5. J. Urgen Schmidhuber
6. Yoshua Bengio
7. Sepp Hochreiter
8. Thomas G. Dietterich
9. Waddah Saeed
10. Eugen Diaconescu

Papers from Peter Tino's perspective

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

Their papers

• Minimum Complexity Echo State Network (2011) — 686 citations
• Financial volatility trading using recurrent neural networks (2001) — 128 citations
• Time-dependent series variance learning with recurrent mixture density networks (2013) — 13 citations
• A Survey on Neural Network Interpretability (2020) — 932 citations
• Managing Diversity in Regression Ensembles (2005) — 410 citations
• Architectural Bias in Recurrent Neural Networks: Fractal Analysis (2002) — 36 citations
• Learning long-term dependencies in NARX recurrent neural networks (1996)
• Learning long-term dependencies is not as difficult with NARX recurrent neural networks (1995)