71 citations · 102 across the 3 of their papers we have counts for
3 papers
cs.LG2017★ 5 cited
Scalable and Incremental Learning of Gaussian Mixture Models
Rafael Pinto, Paulo Engel
This work presents a fast and scalable algorithm for incremental learning of Gaussian mixture models. By performing rank-one updates on its precision matrices and determinants, its…
cs.LG2015★ 71 cited
A Fast Incremental Gaussian Mixture Model
Rafael Pinto, Paulo Engel
This work builds upon previous efforts in online incremental learning, namely the Incremental Gaussian Mixture Network (IGMN). The IGMN is capable of learning from data streams in…
cs.AI2015★ 26 cited
A Framework for Constrained and Adaptive Behavior-Based Agents
Renato de Pontes Pereira, Paulo Martins Engel
Behavior Trees are commonly used to model agents for robotics and games, where constrained behaviors must be designed by human experts in order to guarantee that these agents will…