65 citations · 101 across the 11 of their papers we have counts for
11 papers
Towards Practical 2D Grapevine Bud Detection with Fully Convolutional Networks
Wenceslao Villegas Marset, Diego Sebastián Pérez, Carlos Ariel Díaz +1
In Viticulture, visual inspection of the plant is a necessary task for measuring relevant variables. In many cases, these visual inspections are susceptible to automation through c…
Efficient comparison of independence structures of log-linear models
Jan Strappa, Facundo Bromberg
Log-linear models are a family of probability distributions which capture relationships between variables. They have been proven useful in a wide variety of fields such as epidemio…
Blankets Joint Posterior score for learning Markov network structures
Federico Schlüter, Yanela Strappa, Diego H. Milone +1
Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields. By learning the structure of independences of a do…
Image Classification of Grapevine Buds using Scale-Invariant Features Transform, Bag of Features and Support Vector Machines
Diego Sebastián Pérez, Facundo Bromberg, Carlos Ariel Diaz
In viticulture, there are several applications where bud detection in vineyard images is a necessary task, susceptible of being automated through the use of computer vision methods…
The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences
Alejandro Edera, Yanela Strappa, Facundo Bromberg
Markov networks are models for compactly representing complex probability distributions. They are composed by a structure and a set of numerical weights. The structure qualitativel…
Computer Vision Approach for Low Cost, High Precision Measurement of Grapevine Trunk Diameter in Outdoor Conditions
Diego Sebastián Pérez, Facundo Bromberg, Francisco Gonzalez Antivilo
Trunk diameter is a variable of agricultural interest, used mainly in the prediction of fruit trees production. It is correlated with leaf area and biomass of trees, and consequent…
Efficient Markov Network Structure Discovery Using Independence Tests
Facundo Bromberg, Dimitris Margaritis, Vasant Honavar
We present two algorithms for learning the structure of a Markov network from data: GSMN* and GSIMN. Both algorithms use statistical independence tests to infer the structure by su…
Learning Markov networks with context-specific independences
Alejandro Edera, Federico Schlüter, Facundo Bromberg
Learning the Markov network structure from data is a problem that has received considerable attention in machine learning, and in many other application fields. This work focuses o…
Markov random fields factorization with context-specific independences
Alejandro Edera, Facundo Bromberg, Federico Schlüter
Markov random fields provide a compact representation of joint probability distributions by representing its independence properties in an undirected graph. The well-known Hammersl…
The IBMAP approach for Markov networks structure learning
Federico Schlüter, Facundo Bromberg, Alejandro Edera
In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficie…
Efficient Independence-Based MAP Approach for Robust Markov Networks Structure Discovery
Facundo Bromberg, Federico Schlüter
This work introduces the IB-score, a family of independence-based score functions for robust learning of Markov networks independence structures. Markov networks are a widely used…