Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
Serramazza, Davide Italo; Bacciu, Davide Learning image captioning as a structured transduction task Conference Proceedings of the 23rd International Conference on Engineering Applications of Neural Networks (EANN 2022), vol. 1600, Communications in Computer and Information Science Springer, 2022. Castellana, Daniele; Bacciu, Davide A Tensor Framework for Learning in Structured Domains Journal Article In: Neurocomputing, vol. 470, pp. 405-426, 2022. Castellana, Daniele; Bacciu, Davide Learning from Non-Binary Constituency Trees via Tensor Decomposition Conference PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS (COLING 2020), 2020. Castellana, Daniele; Bacciu, Davide Generalising Recursive Neural Models by Tensor Decomposition Conference Proceedings of the 2020 IEEE World Congress on Computational Intelligence, 2020. Castellana, Daniele; Bacciu, Davide Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data Conference Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020. Davide, Bacciu; Daniele, Castellana Bayesian Mixtures of Hidden Tree Markov Models for Structured Data Clustering Journal Article In: Neurocomputing, vol. 342, pp. 49-59, 2019, ISBN: 0925-2312. Davide, Bacciu; Antonio, Bruno Deep Tree Transductions - A Short Survey Conference Proceedings of the 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) , Recent Advances in Big Data and Deep Learning Springer International Publishing, 2019. Davide, Bacciu; Antonio, Bruno Text Summarization as Tree Transduction by Top-Down TreeLSTM Conference Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI'18), IEEE, 2018. Davide, Bacciu; Daniele, Castellana Learning Tree Distributions by Hidden Markov Models Workshop Proceedings of the FLOC 2018 Workshop on Learning and Automata (LearnAut'18), 2018. Davide, Bacciu; Daniele, Castellana Mixture of Hidden Markov Models as Tree Encoder Conference Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'18), i6doc.com, Louvain-la-Neuve, Belgium, 2018, ISBN: 978-287587047-6. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Generative Kernels for Tree-Structured Data Journal Article In: Neural Networks and Learning Systems, IEEE Transactions on, 2018, ISSN: 2162-2388 . Davide, Bacciu; Claudio, Gallicchio; Alessio, Micheli A reservoir activation kernel for trees Conference Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'16), i6doc.com, 2016, ISBN: 978-287587027-. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Integrating bi-directional contexts in a generative kernel for trees Conference Neural Networks (IJCNN), 2014 International Joint Conference on, IEEE, 2014. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Modeling Bi-directional Tree Contexts by Generative Transductions Conference Neural Information Processing, vol. 8834, Springer International Publishing, 2014. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Compositional Generative Mapping for Tree-Structured Data - Part II: Topographic Projection Model Journal Article In: Neural Networks and Learning Systems, IEEE Transactions on, vol. 24, no. 2, pp. 231 -247, 2013, ISSN: 2162-237X. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Compositional Generative Mapping for Tree-Structured Data; Part I: Bottom-Up Probabilistic Modeling of Trees Journal Article In: Neural Networks and Learning Systems, IEEE Transactions on, vol. 23, no. 12, pp. 1987 -2002, 2012, ISSN: 2162-237X. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti A Generative Multiset Kernel for Structured Data Conference Artificial Neural Networks and Machine Learning - ICANN 2012 proceedings, Springer LNCS series, vol. 7552, Springer-Verlag, BERLIN HEIDELBERG, 2012. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Input-Output Hidden Markov Models for Trees Conference ESANN 2012 - The 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Proceedings, Ciaco scrl - i6doc.com, 2012. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Adaptive Tree Kernel by Multinomial Generative Topographic Mapping Conference Proceedings of the International Joint Conference on Neural Networks, IEEE, Piscataway (NJ), 2011. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti A Bottom-up Hidden Tree Markov Model Technical Report Università di Pisa no. TR-10-08, 2010. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Bottom-Up Generative Modeling of Tree-Structured Data Conference LNCS 6443: Neural Information Processing. Theory and Algorithms. Part I, vol. 6443, Springer-Verlag, BERLIN HEIDELBERG, 2010. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Compositional Generative Mapping of Structured Data Conference Proceedings of the 2010 IEEE InternationalJoint Conference on Neural Networks(IJCNN'10), IEEE, 2010.
@conference{Serramazza2022,
title = {Learning image captioning as a structured transduction task},
author = {Davide Italo Serramazza and Davide Bacciu},
doi = {doi.org/10.1007/978-3-031-08223-8_20},
year = {2022},
date = {2022-06-20},
urldate = {2022-06-20},
booktitle = {Proceedings of the 23rd International Conference on Engineering Applications of Neural Networks (EANN 2022)},
volume = {1600},
pages = {235–246},
publisher = {Springer},
series = {Communications in Computer and Information Science },
abstract = {Image captioning is a task typically approached by deep encoder-decoder architectures, where the encoder component works on a flat representation of the image while the decoder considers a sequential representation of natural language sentences. As such, these encoder-decoder architectures implement a simple and very specific form of structured transduction, that is a generalization of a predictive problem where the input data and output predictions might have substantially different structures and topologies. In this paper, we explore a generalization of such an approach by addressing the problem as a general structured transduction problem. In particular, we provide a framework that allows considering input and output information with a tree-structured representation. This allows taking into account the hierarchical nature underlying both images and sentences. To this end, we introduce an approach to generate tree-structured representations from images along with an autoencoder working with this kind of data. We empirically assess our approach on both synthetic and realistic tasks.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@article{Castellana2021,
title = {A Tensor Framework for Learning in Structured Domains},
author = {Daniele Castellana and Davide Bacciu},
editor = {Kerstin Bunte and Niccolo Navarin and Luca Oneto},
doi = {10.1016/j.neucom.2021.05.110},
year = {2022},
date = {2022-01-22},
urldate = {2022-01-22},
journal = {Neurocomputing},
volume = {470},
pages = {405-426},
abstract = {Learning machines for structured data (e.g., trees) are intrinsically based on their capacity to learn representations by aggregating information from the multi-way relationships emerging from the structure topology. While complex aggregation functions are desirable in this context to increase the expressiveness of the learned representations, the modelling of higher-order interactions among structure constituents is unfeasible, in practice, due to the exponential number of parameters required. Therefore, the common approach is to define models which rely only on first-order interactions among structure constituents.
In this work, we leverage tensors theory to define a framework for learning in structured domains. Such a framework is built on the observation that more expressive models require a tensor parameterisation. This observation is the stepping stone for the application of tensor decompositions in the context of recursive models. From this point of view, the advantage of using tensor decompositions is twofold since it allows limiting the number of model parameters while injecting inductive biases that do not ignore higher-order interactions.
We apply the proposed framework on probabilistic and neural models for structured data, defining different models which leverage tensor decompositions. The experimental validation clearly shows the advantage of these models compared to first-order and full-tensorial models.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this work, we leverage tensors theory to define a framework for learning in structured domains. Such a framework is built on the observation that more expressive models require a tensor parameterisation. This observation is the stepping stone for the application of tensor decompositions in the context of recursive models. From this point of view, the advantage of using tensor decompositions is twofold since it allows limiting the number of model parameters while injecting inductive biases that do not ignore higher-order interactions.
We apply the proposed framework on probabilistic and neural models for structured data, defining different models which leverage tensor decompositions. The experimental validation clearly shows the advantage of these models compared to first-order and full-tensorial models.
@conference{CastellanaCOLING2020,
title = {Learning from Non-Binary Constituency Trees via Tensor Decomposition},
author = {Daniele Castellana and Davide Bacciu},
year = {2020},
date = {2020-12-08},
urldate = {2020-12-08},
booktitle = {PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS (COLING 2020)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{Wcci20Tensor,
title = {Generalising Recursive Neural Models by Tensor Decomposition},
author = {Daniele Castellana and Davide Bacciu},
url = {https://arxiv.org/abs/2006.10021, Arxiv},
year = {2020},
date = {2020-07-19},
urldate = {2020-07-19},
booktitle = {Proceedings of the 2020 IEEE World Congress on Computational Intelligence},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{esann20Castellana,
title = { Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data },
author = {Daniele Castellana and Davide Bacciu},
editor = {Michel Verleysen},
url = {https://arxiv.org/pdf/2006.10619.pdf, Arxiv},
year = {2020},
date = {2020-04-21},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@article{neucomBayesHTMM,
title = {Bayesian Mixtures of Hidden Tree Markov Models for Structured Data Clustering},
author = {Bacciu Davide and Castellana Daniele},
url = {https://doi.org/10.1016/j.neucom.2018.11.091},
doi = {10.1016/j.neucom.2018.11.091},
isbn = {0925-2312},
year = {2019},
date = {2019-05-21},
journal = {Neurocomputing},
volume = {342},
pages = {49-59},
abstract = {The paper deals with the problem of unsupervised learning with structured data, proposing a mixture model approach to cluster tree samples. First, we discuss how to use the Switching-Parent Hidden Tree Markov Model, a compositional model for learning tree distributions, to define a finite mixture model where the number of components is fixed by a hyperparameter. Then, we show how to relax such an assumption by introducing a Bayesian non-parametric mixture model where the number of necessary hidden tree components is learned from data. Experimental validation on synthetic and real datasets show the benefit of mixture models over simple hidden tree models in clustering applications. Further, we provide a characterization of the behaviour of the two mixture models for different choices of their hyperparameters.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{inns2019,
title = {Deep Tree Transductions - A Short Survey},
author = {Bacciu Davide and Bruno Antonio},
editor = {Luca Oneto and Nicol{`o} Navarin and Alessandro Sperduti and Davide Anguita},
url = {https://arxiv.org/abs/1902.01737},
doi = {10.1007/978-3-030-16841-4_25},
year = {2019},
date = {2019-01-04},
urldate = {2019-01-04},
booktitle = {Proceedings of the 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) },
pages = {236--245},
publisher = {Springer International Publishing},
series = {Recent Advances in Big Data and Deep Learning},
abstract = {The paper surveys recent extensions of the Long-Short Term Memory networks to handle tree structures from the perspective of learning non-trivial forms of isomorph structured transductions. It provides a discussion of modern TreeLSTM models, showing the effect of the bias induced by the direction of tree processing. An empirical analysis is performed on real-world benchmarks, highlighting how there is no single model adequate to effectively approach all transduction problems.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{ssci2018,
title = {Text Summarization as Tree Transduction by Top-Down TreeLSTM},
author = {Bacciu Davide and Bruno Antonio},
url = {https://arxiv.org/abs/1809.09096},
doi = {10.1109/SSCI.2018.8628873},
year = {2018},
date = {2018-11-18},
urldate = {2018-11-18},
booktitle = {Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI'18)},
pages = {1411-1418},
publisher = {IEEE},
abstract = {Extractive compression is a challenging natural language processing problem. This work contributes by formulating neural extractive compression as a parse tree transduction problem, rather than a sequence transduction task. Motivated by this, we introduce a deep neural model for learning structure-to-substructure tree transductions by extending the standard Long Short-Term Memory, considering the parent-child relationships in the structural recursion. The proposed model can achieve state of the art performance on sentence compression benchmarks, both in terms of accuracy and compression rate. },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@workshop{learnaut18,
title = {Learning Tree Distributions by Hidden Markov Models},
author = {Bacciu Davide and Castellana Daniele},
editor = {Rémi Eyraud and Jeffrey Heinz and Guillaume Rabusseau and Matteo Sammartino },
url = {https://arxiv.org/abs/1805.12372},
year = {2018},
date = {2018-07-13},
booktitle = {Proceedings of the FLOC 2018 Workshop on Learning and Automata (LearnAut'18)},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@conference{esann2018Tree,
title = {Mixture of Hidden Markov Models as Tree Encoder},
author = {Bacciu Davide and Castellana Daniele},
editor = {Michel Verleysen},
isbn = {978-287587047-6},
year = {2018},
date = {2018-04-26},
urldate = {2018-04-26},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'18)},
pages = {543-548},
publisher = {i6doc.com},
address = {Louvain-la-Neuve, Belgium},
abstract = {The paper introduces a new probabilistic tree encoder based on a mixture of Bottom-up Hidden Tree Markov Models. The ability to recognise similar structures in data is experimentally assessed both in clusterization and classification tasks. The results of these preliminary experiments suggest that the model can be successfully used to compress the tree structural and label patterns in a vectorial representation.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@article{tnnlsTreeKer17,
title = {Generative Kernels for Tree-Structured Data},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1109/TNNLS.2017.2785292},
issn = {2162-2388 },
year = {2018},
date = {2018-01-15},
journal = {Neural Networks and Learning Systems, IEEE Transactions on},
abstract = {The paper presents a family of methods for the design of adaptive kernels for tree-structured data that exploits the summarization properties of hidden states of hidden Markov models for trees. We introduce a compact and discriminative feature space based on the concept of hidden states multisets and we discuss different approaches to estimate such hidden state encoding. We show how it can be used to build an efficient and general tree kernel based on Jaccard similarity. Further, we derive an unsupervised convolutional generative kernel using a topology induced on the Markov states by a tree topographic mapping. The paper provides an extensive empirical assessment on a variety of structured data learning tasks, comparing the predictive accuracy and computational efficiency of state-of-the-art generative, adaptive and syntactical tree kernels. The results show that the proposed generative approach has a good tradeoff between computational complexity and predictive performance, in particular when considering the soft matching introduced by the topographic mapping.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{esann2016,
title = {A reservoir activation kernel for trees},
author = {Bacciu Davide and Gallicchio Claudio and Micheli Alessio
},
editor = {M. Verleysen},
url = {https://www.researchgate.net/profile/Claudio_Gallicchio/publication/313236954_A_Reservoir_Activation_Kernel_for_Trees/links/58a9db0892851cf0e3c6b8df/A-Reservoir-Activation-Kernel-for-Trees.pdf},
isbn = {978-287587027-},
year = {2016},
date = {2016-04-29},
urldate = {2016-04-29},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'16)},
pages = {29-34},
publisher = { i6doc.com},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{11568_586070,
title = {Integrating bi-directional contexts in a generative kernel for trees},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1109/IJCNN.2014.6889768},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Neural Networks (IJCNN), 2014 International Joint Conference on},
pages = {4145--4151},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{11568_665864,
title = {Modeling Bi-directional Tree Contexts by Generative Transductions},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
url = {http://dx.doi.org/10.1007/978-3-319-12637-1_68},
doi = {10.1007/978-3-319-12637-1_68},
year = {2014},
date = {2014-01-01},
booktitle = {Neural Information Processing},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {8834},
pages = {543--550},
publisher = {Springer International Publishing},
abstract = {We introduce an approach to integrate bi-directional contexts in a generative tree model by means of structured transductions. We show how this can be efficiently realized as the composition of a top-down and a bottom-up generative model for trees, that are trained independently within a circular encoding-decoding scheme. The resulting input-driven generative model is shown to capture information concerning bi-directional contexts within its state-space. An experimental evaluation using the Jaccard generative kernel for trees is presented, indicating that the approach can achieve state of the art performance on tree classification benchmarks.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@article{gmtsdII2012,
title = {Compositional Generative Mapping for Tree-Structured Data - Part II: Topographic Projection Model},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6395856},
doi = {10.1109/TNNLS.2012.2228226},
issn = {2162-237X},
year = {2013},
date = {2013-02-01},
journal = {Neural Networks and Learning Systems, IEEE Transactions on},
volume = {24},
number = {2},
pages = {231 -247},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{gmtsdI2012,
title = {Compositional Generative Mapping for Tree-Structured Data; Part I: Bottom-Up Probabilistic Modeling of Trees},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6353263},
doi = {10.1109/TNNLS.2012.2222044},
issn = {2162-237X},
year = {2012},
date = {2012-12-01},
journal = {Neural Networks and Learning Systems, IEEE Transactions on},
volume = {23},
number = {12},
pages = {1987 -2002},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{11568_156516,
title = {A Generative Multiset Kernel for Structured Data},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1007/978-3-642-33269-2_8},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
booktitle = {Artificial Neural Networks and Machine Learning - ICANN 2012 proceedings, Springer LNCS series},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {7552},
pages = {57--64},
publisher = {Springer-Verlag},
address = {BERLIN HEIDELBERG},
abstract = {The paper introduces a novel approach for defining efficient generative kernels for structured-data based on the concept of multisets and Jaccard similarity. The multiset feature-space allows to enhance the adaptive kernel with syntactic information on structure matching. The proposed approach is validated using an input-driven hidden Markov model for trees as generative model, but it is enough general to be straightforwardly applicable to any probabilistic latent variable model. The experimental evaluation shows that the proposed Jaccard kernel has a superior classification performance with respect to the Fisher Kernel, while consistently reducing the computational requirements.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{11568_152836,
title = {Input-Output Hidden Markov Models for Trees},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
booktitle = {ESANN 2012 - The 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Proceedings},
pages = {25--30},
publisher = {Ciaco scrl - i6doc.com},
abstract = {The paper introduces an input-driven generative model for tree-structured data that extends the bottom-up hidden tree Markov model with non-homogenous transition and emission probabilities. The advantage of introducing an input-driven dynamics in structured-data pro- cessing is experimentally investigated. The results of this preliminary analysis suggest that input-driven models can capture more discrimina- tive structural information than non-input-driven approaches.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{11568_145907,
title = {Adaptive Tree Kernel by Multinomial Generative Topographic Mapping},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6033423&contentType=Conference+Publications&refinements%3D4294413850%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6033131%29},
doi = {10.1109/IJCNN.2011.6033423},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {Proceedings of the International Joint Conference on Neural Networks},
pages = {1651--1658},
publisher = {IEEE},
address = {Piscataway (NJ)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@techreport{11568_254437,
title = {A Bottom-up Hidden Tree Markov Model},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
url = {http://compass2.di.unipi.it/TR/Files/TR-10-08.pdf.gz},
year = {2010},
date = {2010-04-01},
urldate = {2010-04-01},
volume = {TR-10-08},
number = {TR-10-08},
pages = {1--22},
institution = {Università di Pisa},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
@conference{11568_142187,
title = {Bottom-Up Generative Modeling of Tree-Structured Data},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1007/978-3-642-17537-4_80},
year = {2010},
date = {2010-01-01},
booktitle = {LNCS 6443: Neural Information Processing. Theory and Algorithms. Part I},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {6443},
pages = {660--668},
publisher = {Springer-Verlag},
address = {BERLIN HEIDELBERG},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{11568_136433,
title = {Compositional Generative Mapping of Structured Data},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1109/IJCNN.2010.5596606},
year = {2010},
date = {2010-01-01},
urldate = {2010-01-01},
booktitle = {Proceedings of the 2010 IEEE InternationalJoint Conference on Neural Networks(IJCNN'10)},
pages = {1359--1366},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}