journals.bib

@article{FraLamRig21-ML-IJ,
  title = {Symbolic {DNN-Tuner}},
  author = {Michele Fraccaroli and
               Evelina Lamma and
               Fabrizio Riguzzi},
  journal = {Machine Learning},
  publisher = {Springer},
  copyright = {Springer},
  year = {2021},
  abstract = {Hyper-Parameter Optimization (HPO) occupies a fundamental role
in Deep Learning systems due to the number of hyper-parameters (HPs) to be
set. The state-of-the-art of HPO methods are Grid Search, Random Search and
Bayesian Optimization. The  rst two methods try all possible combinations
and random combination of the HPs values, respectively. This is performed in
a blind manner, without any information for choosing the new set of HPs val-
ues. Bayesian Optimization (BO), instead, keeps track of past results and uses
them to build a probabilistic model mapping HPs into a probability density of
the objective function. Bayesian Optimization builds a surrogate probabilistic
model of the objective function,  nds the HPs values that perform best on the
surrogate model and updates it with new results. In this paper, we improve BO
applied to Deep Neural Network (DNN) by adding an analysis of the results
of the network on training and validation sets. This analysis is performed by
exploiting rule-based programming, and in particular by using Probabilistic
Logic Programming. The resulting system, called Symbolic DNN-Tuner, logi-
cally evaluates the results obtained from the training and the validation phase
and, by applying symbolic tuning rules,  xes the network architecture, and its
HPs, therefore improving performance. We also show the e ectiveness of the
proposed approach, by an experimental evaluation on literature and real-life
datasets.},
  keywords = {Deep Learning   Hyper-Parameter Optimization   Probabilistic
Logic Programming},
  doi = {10.1007/s10994-021-06097-1},
  isbn = {1573-0565}
}
@article{FraLamRig2022-SwX-IJ,
  title = {Symbolic {DNN-Tuner}: A {Python} and {ProbLog}-based system for optimizing Deep Neural Networks hyperparameters},
  journal = {SoftwareX},
  volume = {17},
  pages = {100957},
  year = {2022},
  issn = {2352-7110},
  doi = {10.1016/j.softx.2021.100957},
  url = {https://www.sciencedirect.com/science/article/pii/S2352711021001825},
  author = {Michele Fraccaroli and Evelina Lamma and Fabrizio Riguzzi},
  keywords = {Deep learning, Probabilistic Logic Programming, Hyper-parameters tuning, Neural-symbolic integration},
  abstract = {The application of deep learning models to increasingly complex contexts has led to a rise in the complexity of the models themselves. Due to this, there is an increase in the number of hyper-parameters (HPs) to be set and Hyper-Parameter Optimization (HPO) algorithms occupy a fundamental role in deep learning. Bayesian Optimization (BO) is the state-of-the-art of HPO for deep learning models. BO keeps track of past results and uses them to build a probabilistic model, building a probability density of HPs. This work aims to improve BO applied to Deep Neural Networks (DNNs) by an analysis of the results of the network on training and validation sets. This analysis is obtained by applying symbolic tuning rules, implemented in Probabilistic Logic Programming (PLP). The resulting system, called Symbolic DNN-Tuner, logically evaluates the results obtained from the training and the validation phase and, by applying symbolic tuning rules, fixes the network architecture, and its HPs, leading to improved performance. In this paper, we present the general system and its implementation. We also show its graphical interface and a simple example of execution.}
}

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