Tarek R. Besold
Artificial Intelligence · 2015–2020
A researcher in the Science corpus: 6 papers; trusts 33 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 99 citations into it; #17,679 of 35,345 by trust here, #9,968 by citations
- Safety, Robustness & Interpretability — 32 citations into it; #1,177 of 2,251 by trust here, #1,312 by citations
- Computer Vision and Pattern Recognition — 27 citations into it; #11,179 of 21,575 by trust here, #10,154 by citations
- Information Systems — 26 citations into it
- Model Architectures — 11 citations into it
- Management Science and Operations Research — 10 citations into it
- Knowledge Graphs & Representation — 7 citations into it
- Representation & Self-Supervised Learning — 6 citations into it
Whom Tarek R. Besold trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- A.S. d'Avila Garcez
- Geoffrey E. Hinton
- Leslie G. Valiant
- Tijmen Tieleman
- Jerome A. Feldman
- Ruslan Salakhutdinov
- Alex Graves
- Geoffrey G. Towell
- Yoshua Bengio
- L. C. Lamb
Who trusts Tarek R. Besold overall
- Daniel S. Weld
- David Demeter
- Giulia Vilone
- Roberto Confalonieri
- Derek Doran
- Nadia Burkart
- Andreas Holzinger
- Longo Luca
Papers from Tarek R. Besold's perspective
Ordered by how strongly the researchers Tarek R. Besold trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Neural-Symbolic Learning and Reasoning: A Survey and Interpretation (2017) — 472 citations
- How Does Predicate Invention Affect Human Comprehensibility? (2016) — 44 citations
- A historical perspective of explainable Artificial Intelligence (2020) — 324 citations
- What Does Explainable AI Really Mean? A New Conceptualization of Perspectives (2017) — 483 citations
- Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples (2017) — 25 citations
- Neural-Symbolic Learning and Reasoning: Contributions and Challenges (2015) — 201 citations