Gerson Zaverucha

Artificial Intelligence · 1999–2017

A researcher in the Science corpus: 5 papers; trusts 23 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it; #8,099 of 35,345 by trust here, #10,566 by citations
• Model Architectures — 10 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it
• Knowledge Graphs & Representation — 7 citations into it; #73 of 226 by trust here, #132 by citations
• Optimization & Training — 6 citations into it
• Computational Theory and Mathematics — 5 citations into it; #2,903 of 12,182 by trust here, #2,419 by citations

Whom Gerson Zaverucha trusts overall

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

1. Gordon D. Plotkin
2. J. Ross Quinlan
3. Stephen H. Muggleton
4. Krzysztof R. Apt
5. Geoffrey G. Towell
6. David E. Rumelhart
7. Martin Gebser
8. Jude W. Shavlik
9. Luc De Raedt
10. Matthew Richardson

Who trusts Gerson Zaverucha overall

1. A.S. d'Avila Garcez
2. Tim Rocktaschel
3. Son Dinh Tran
4. Tong Wang
5. Stefano V. Albrecht
6. L. C. Lamb
7. Lior Friedman
8. Marta Garnelo
9. John K. Feser
10. Sebastian Riedel

Papers from Gerson Zaverucha's perspective

Ordered by how strongly the researchers Gerson Zaverucha trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Neural Turing Machines
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Learning internal representations by error propagation
5. Show and tell: A neural image caption generator
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. Deep Boltzmann Machines
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Inductive Logic Programming (2014) — 719 citations
• Neural-Symbolic Learning and Reasoning: A Survey and Interpretation (2017) — 472 citations
• Learning Theories Using Estimation Distribution Algorithms and (Reduced) Bottom Clauses (2011) — 13 citations
• Fast relational learning using bottom clause propositionalization with artificial neural networks (2013) — 138 citations
• The Connectionist Inductive Learning and Logic Programming System (1999)