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VLDB 2026 Research / reviewers in the wild / expert

Fernanda Botelho

dblp:69/3911 · DBLP profile ↗
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8ranked-venue papers
2as first author
0since 2021 · last 2011
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 6 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1Theory of computation · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
3 papers
Trustworthy machine learning · 38% Deep learning architectures and training · 25% Motion planning and robot control · 19%
Theoretical computer science
1 paper
Graph algorithms and graph theory · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning
interpretability		 0.021993
Stability and Observability · NIPS 1993
Observability of Neural Network Behavior · NIPS 1993
Machine learning › Deep learning architectures and training › equivariant neural network
symmetric neural networks		 0.011995
Absence of Cycles in Symmetric Neural Networks · NIPS 1995
Robotics › Robot navigation and mapping › state estimation
observability analysis		 0.011993
Stability and Observability · NIPS 1993
Robotics › Motion planning and robot control
stability analysis		 0.011993
Stability and Observability · NIPS 1993
YearPublicationVenuePosition
2011 TROCAS: Communication Skills Development in Children with Autism Spectrum Disorders via ICT
Margarida Lucas da Silva, Carla Simões, Daniel Gonçalves 0002, Tiago João Vieira Guerreiro, Hugo Silva 0001, Fernanda Botelho
 INTERACT (4)6
1999 Dynamical approximation by recurrent neural networks
Max H. Garzon, Fernanda Botelho
 Neurocomputing2
1999 Dynamical features simulated by recurrent neural networks
Fernanda Botelho
 Neural Networks1
1998 Absence of Cycles in Symmetric Neural Networks
abstract
For a given recurrent neural network, a discrete-time model may have asymptotic dynamics different from the one of a related continuous-time model. In this article, we consider a discrete-time model that discretizes the continuous-time leaky integrat or model and study its parallel, sequential, block-sequential, and distributed dynamics for symmetric networks. We provide sufficient (and in many cases necessary) conditions for the discretized model to have the same cycle-free dynamics of the corresponding continuous-time model in symmetric networks.
Arun K. Jagota, Fernanda Botelho, Max H. Garzon
 Neural Comput.3
1995 Absence of Cycles in Symmetric Neural Networks
Arun K. Jagota, Fernanda Botelho, Max H. Garzon
 NIPS3
1994 Boolean Neural Nets are Observable
Fernanda Botelho, Max H. Garzon
 Theor. Comput. Sci.1
1993 Observability of Neural Network Behavior
Max H. Garzon, Fernanda Botelho
 NIPS2
1993 Stability and Observability
Max H. Garzon, Fernanda Botelho
 NIPS2