@article{pandelea2022,

title = {Modeling Mood Polarity and Declaration Occurrence by Neural Temporal Point Processes},

author = {Davide Bacciu and Davide Morelli and Vlad Pandelea},

doi = {10.1109/TNNLS.2022.3172871},

year  = {2022},

date = {2022-05-13},

urldate = {2022-05-13},

journal = {IEEE Transactions on Neural Networks and Learning Systems},

pages = {1-8},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bacciu2022,

title = {Explaining Deep Graph Networks via Input Perturbation},

author = {Davide Bacciu and Danilo Numeroso

},

doi = {10.1109/TNNLS.2022.3165618},

year  = {2022},

date = {2022-04-21},

urldate = {2022-04-21},

journal = {IEEE Transactions on Neural Networks and Learning Systems},

abstract = {Deep Graph Networks are a family of machine learning models for structured data which are finding heavy application in life-sciences (drug repurposing, molecular property predictions) and on social network data (recommendation systems). The privacy and safety-critical nature of such domains motivates the need for developing effective explainability methods for this family of models. So far, progress in this field has been challenged by the combinatorial nature and complexity of graph structures. In this respect, we present a novel local explanation framework specifically tailored to graph data and deep graph networks. Our approach leverages reinforcement learning to generate meaningful local perturbations of the input graph, whose prediction we seek an interpretation for. These perturbed data points are obtained by optimising a multi-objective score taking into account similarities both at a structural level as well as at the level of the deep model outputs. By this means, we are able to populate a set of informative neighbouring samples for the query graph, which is then used to fit an interpretable model for the predictive behaviour of the deep network locally to the query graph prediction. We show the effectiveness of the proposed explainer by a qualitative analysis on two chemistry datasets, TOS and ESOL and by quantitative results on a benchmark dataset for explanations, CYCLIQ.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Collodi2022,

title = {Learning with few examples the semantic description of novel human-inspired grasp strategies from RGB data},

author = { Lorenzo Collodi and Davide Bacciu and Matteo Bianchi and Giuseppe Averta},

url = {https://www.researchgate.net/profile/Giuseppe-Averta/publication/358006552_Learning_With_Few_Examples_the_Semantic_Description_of_Novel_Human-Inspired_Grasp_Strategies_From_RGB_Data/links/61eae01e8d338833e3857251/Learning-With-Few-Examples-the-Semantic-Description-of-Novel-Human-Inspired-Grasp-Strategies-From-RGB-Data.pdf, Open Version},

doi = {https://doi.org/10.1109/LRA.2022.3144520},

year  = {2022},

date = {2022-04-04},

urldate = {2022-04-04},

journal = { IEEE Robotics and Automation Letters},

pages = { 2573 - 2580},

publisher = {IEEE},

abstract = {Data-driven approaches and human inspiration are fundamental to endow robotic manipulators with advanced autonomous grasping capabilities. However, to capitalize upon these two pillars, several aspects need to be considered, which include the number of human examples used for training; the need for having in advance all the required information for classification (hardly feasible in unstructured environments); the trade-off between the task performance and the processing cost. In this paper, we propose a RGB-based pipeline that can identify the object to be grasped and guide the actual execution of the grasping primitive selected through a combination of Convolutional and Gated Graph Neural Networks. We consider a set of human-inspired grasp strategies, which are afforded by the geometrical properties of the objects and identified from a human grasping taxonomy, and propose to learn new grasping skills with only a few examples. We test our framework with a manipulator endowed with an under-actuated soft robotic hand. Even though we use only 2D information to minimize the footprint of the network, we achieve 90% of successful identifications of the most appropriate human-inspired grasping strategy over ten different classes, of which three were few-shot learned, outperforming an ideal model trained with all the classes, in sample-scarce conditions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gravina2022,

title = {Controlling astrocyte-mediated synaptic pruning signals for schizophrenia drug repurposing with Deep Graph Networks},

author = {Alessio Gravina and Jennifer L. Wilson and Davide Bacciu and Kevin J. Grimes and Corrado Priami},

url = {https://www.biorxiv.org/content/10.1101/2021.10.07.463459v1, BioArxiv},

doi = {doi.org/10.1371/journal.pcbi.1009531},

year  = {2022},

date = {2022-04-01},

urldate = {2022-04-01},

journal = {Plos Computational Biology},

volume = {18},

number = {5},

abstract = {Schizophrenia is a debilitating psychiatric disorder, leading to both physical and social morbidity.  Worldwide 1% of the population is struggling with the disease, with 100,000 new cases annually only in the United States. Despite its importance, the goal of finding effective treatments for schizophrenia remains a challenging task, and previous work conducted expensive large-scale phenotypic screens. This work investigates the benefits of Machine Learning for graphs to optimize drug phenotypic screens and predict compounds that mitigate abnormal brain reduction induced by excessive glial phagocytic activity in schizophrenia subjects. Given a compound and its concentration as input, we propose a method that predicts a score associated with three possible compound effects, ie reduce, increase, or not influence phagocytosis.  We leverage a high-throughput screening to prove experimentally that our method achieves good generalization capabilities. The screening involves 2218 compounds at five different concentrations. Then, we analyze the usability of our approach in a practical setting, ie prioritizing the selection of compounds in the SWEETLEAD library. We provide a list of 64 compounds from the library that have the most potential clinical utility for glial phagocytosis mitigation. Lastly, we propose a novel approach to computationally validate their utility as possible therapies for schizophrenia.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{Carta2022,

title = {Catastrophic Forgetting in Deep Graph Networks: a Graph Classification benchmark},

author = {Antonio Carta and Andrea Cossu and Federico Errica and Davide Bacciu},

doi = {10.3389/frai.2022.824655},

year  = {2022},

date = {2022-01-11},

urldate = {2022-01-11},

journal = {Frontiers in Artificial Intelligence },

abstract = { In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether classical continual learning techniques for flat and sequential data have a tangible impact on performances when applied to graph data. To do so, we experiment with a structure-agnostic model and a deep graph network in a robust and controlled environment on three different datasets. The benchmark is complemented by an investigation on the effect of structure-preserving regularization techniques on catastrophic forgetting. We find that replay is the most effective strategy in so far, which also benefits the most from the use of regularization. Our findings suggest interesting future research at the intersection of the continual and graph representation learning fields. Finally, we provide researchers with a flexible software framework to reproduce our results and carry out further experiments. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.3389/frai.2022.829842,

title = {Is Class-Incremental Enough for Continual Learning?},

author = {Andrea Cossu and Gabriele Graffieti and Lorenzo Pellegrini and Davide Maltoni and Davide Bacciu and Antonio Carta and Vincenzo Lomonaco},

url = {https://www.frontiersin.org/article/10.3389/frai.2022.829842},

doi = {10.3389/frai.2022.829842},

issn = {2624-8212},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Frontiers in Artificial Intelligence},

volume = {5},

abstract = {The ability of a model to learn continually can be empirically assessed in different continual learning scenarios. Each scenario defines the constraints and the opportunities of the learning environment. Here, we challenge the current trend in the continual learning literature to experiment mainly on class-incremental scenarios, where classes present in one experience are never revisited. We posit that an excessive focus on this setting may be limiting for future research on continual learning, since class-incremental scenarios artificially exacerbate catastrophic forgetting, at the expense of other important objectives like forward transfer and computational efficiency. In many real-world environments, in fact, repetition of previously encountered concepts occurs naturally and contributes to softening the disruption of previous knowledge. We advocate for a more in-depth study of alternative continual learning scenarios, in which repetition is integrated by design in the stream of incoming information. Starting from already existing proposals, we describe the advantages such class-incremental with repetition scenarios could offer for a more comprehensive assessment of continual learning models.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{atzeni2022,

title = {A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques},

author = {Daniele Atzeni and Davide Bacciu and Daniele Mazzei and Giuseppe Prencipe},

url = {https://www.mdpi.com/1424-8220/22/13/4925},

doi = {10.3390/s22134925},

issn = {1424-8220},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Sensors},

volume = {22},

number = {13},

abstract = {Wireless networks have drastically influenced our lifestyle, changing our workplaces and society. Among the variety of wireless technology, Wi-Fi surely plays a leading role, especially in local area networks. The spread of mobiles and tablets, and more recently, the advent of Internet of Things, have resulted in a multitude of Wi-Fi-enabled devices continuously sending data to the Internet and between each other. At the same time, Machine Learning has proven to be one of the most effective and versatile tools for the analysis of fast streaming data. This systematic review aims at studying the interaction between these technologies and how it has developed throughout their lifetimes. We used Scopus, Web of Science, and IEEE Xplore databases to retrieve paper abstracts and leveraged a topic modeling technique, namely, BERTopic, to analyze the resulting document corpus. After these steps, we inspected the obtained clusters and computed statistics to characterize and interpret the topics they refer to. Our results include both the applications of Wi-Fi sensing and the variety of Machine Learning algorithms used to tackle them. We also report how the Wi-Fi advances have affected sensing applications and the choice of the most suitable Machine Learning models.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cossu2021b,

title = {Continual Learning for Recurrent Neural Networks: an Empirical Evaluation},

author = {Andrea Cossu and Antonio Carta and Vincenzo Lomonaco and Davide Bacciu},

url = {https://arxiv.org/abs/2103.07492, Arxiv},

year  = {2021},

date = {2021-12-03},

urldate = {2021-12-03},

journal = {Neural Networks},

volume = {143},

pages = {607-627},

abstract = {     Learning continuously during all model lifetime is fundamental to deploy machine learning solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with recurrent neural networks could pave the way to a large number of applications where incoming data is non stationary, like natural language processing and robotics. However, the existing body of work on the topic is still fragmented, with approaches which are application-specific and whose assessment is based on heterogeneous learning protocols and datasets. In this paper, we organize the literature on CL for sequential data processing by providing a categorization of the contributions and a review of the benchmarks. We propose two new benchmarks for CL with sequential data based on existing datasets, whose characteristics resemble real-world applications. We also provide a broad empirical evaluation of CL and Recurrent Neural Networks in class-incremental scenario, by testing their ability to mitigate forgetting with a number of different strategies which are not specific to sequential data processing. Our results highlight the key role played by the sequence length and the importance of a clear specification of the CL scenario. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Carta2021b,

title = {Encoding-based Memory for Recurrent Neural Networks},

author = {Antonio Carta and Alessandro Sperduti and Davide Bacciu},

url = {https://arxiv.org/abs/2001.11771, Arxiv},

doi = {10.1016/j.neucom.2021.04.051},

year  = {2021},

date = {2021-10-07},

urldate = {2021-10-07},

journal = {Neurocomputing},

volume = {456},

pages = {407-420},

publisher = {Elsevier},

abstract = {Learning to solve sequential tasks with recurrent models requires the ability to memorize long sequences and to extract task-relevant features from them. In this paper, we study the memorization subtask from the point of view of the design and training of recurrent neural networks. We propose a new model, the Linear Memory Network, which features an encoding-based memorization component built with a linear autoencoder for sequences. We extend the memorization component with a modular memory that encodes the hidden state sequence at different sampling frequencies. Additionally, we provide a specialized training algorithm that initializes the memory to efficiently encode the hidden activations of the network. The experimental results on synthetic and real-world datasets show that specializing the training algorithm to train the memorization component always improves the final performance whenever the memorization of long sequences is necessary to solve the problem. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Averta2021,

title = {Learning to Prevent Grasp Failure with Soft Hands: From Online Prediction to Dual-Arm Grasp Recovery},

author = {Giuseppe Averta and Federica Barontini and Irene Valdambrini and Paolo Cheli and Davide Bacciu and Matteo Bianchi},

doi = {10.1002/aisy.202100146},

year  = {2021},

date = {2021-10-07},

urldate = {2021-10-07},

journal = {Advanced Intelligent Systems},

abstract = {Soft hands allow to simplify the grasp planning to achieve a successful grasp, thanks to their intrinsic adaptability. At the same time, their usage poses new challenges, related to the adoption of classical sensing techniques originally developed for rigid end defectors, which provide fundamental information, such as to detect object slippage. Under this regard, model-based approaches for the processing of the gathered information are hard to use, due to the difficulties in modeling hand–object interaction when softness is involved. To overcome these limitations, in this article, we proposed to combine distributed tactile sensing and machine learning (recurrent neural network) to detect sliding conditions for a soft robotic hand mounted on a robotic manipulator, targeting the prediction of the grasp failure event and the direction of sliding. The outcomes of these predictions allow for an online triggering of a compensatory action performed with a second robotic arm–hand system, to prevent the failure. Despite the fact that the network is trained only with spherical and cylindrical objects, we demonstrate high generalization capabilities of our framework, achieving a correct prediction of the failure direction in 75% of cases, and a 85% of successful regrasps, for a selection of 12 objects of common use.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bacciu2021b,

title = {K-Plex Cover Pooling for Graph Neural Networks},

author = {Davide Bacciu and Alessio Conte and Roberto Grossi and Francesco Landolfi and Andrea Marino},

editor = {Annalisa Appice and Sergio Escalera and José A. Gámez and Heike Trautmann},

url = {https://link.springer.com/article/10.1007/s10618-021-00779-z, Published version},

doi = {10.1007/s10618-021-00779-z},

year  = {2021},

date = {2021-09-13},

urldate = {2021-09-13},

journal = {Data Mining and Knowledge Discovery},

abstract = {raph pooling methods provide mechanisms for structure reduction that are intended to ease the diffusion of context between nodes further in the graph, and that typically leverage community discovery mechanisms or node and edge pruning heuristics. In this paper, we introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity patterns. Our pooling method, named KPlexPool, builds on the concepts of graph covers and k-plexes, i.e. pseudo-cliques where each node can miss up to k links. The experimental evaluation on benchmarks on molecular and social graph classification shows that KPlexPool achieves state of the art performances against both parametric and non-parametric pooling methods in the literature, despite generating pooled graphs based solely on topological information.},

note = {Accepted also as paper to the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2021)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Resta2021,

title = { Occlusion-based Explanations in Deep Recurrent Models for Biomedical Signals },

author = {Michele Resta and Anna Monreale and Davide Bacciu},

editor = {Fabio Aiolli and Mirko Polato},

doi = {10.3390/e23081064},

year  = {2021},

date = {2021-09-01},

urldate = {2021-09-01},

journal = {Entropy},

volume = {23},

number = {8},

pages = {1064},

abstract = { The biomedical field is characterized by an ever-increasing production of sequential data, which often come under the form of biosignals capturing the time-evolution of physiological processes, such as blood pressure and brain activity. This has motivated a large body of research dealing with the development of machine learning techniques for the predictive analysis of such biosignals. Unfortunately, in high-stakes decision making, such as clinical diagnosis, the opacity of machine learning models becomes a crucial aspect to be addressed in order to increase the trust and adoption of AI technology. In this paper we propose a model agnostic explanation method, based on occlusion, enabling the learning of the input influence on the model predictions. We specifically target problems involving the predictive analysis of time-series data and the models which are typically used to deal with data of such nature, i.e. recurrent neural networks. Our approach is able to provide two different kinds of explanations: one suitable for technical experts, who need to verify the quality and correctness of machine learning models, and one suited to physicians, who need to understand the rationale underlying the prediction to take aware decisions. A wide experimentation on different physiological data demonstrate the effectiveness of our approach, both in classification and regression tasks. },

note = {Special issue on Representation Learning},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{errica_deep_2021,

title = {A deep graph network-enhanced sampling approach to efficiently explore the space of reduced representations of proteins},

author = {Federico Errica and Marco Giulini and Davide Bacciu and Roberto Menichetti and Alessio Micheli and Raffaello Potestio},

doi = {10.3389/fmolb.2021.637396},

year  = {2021},

date = {2021-02-28},

urldate = {2021-02-28},

journal = {Frontiers in Molecular Biosciences},

volume = {8},

pages = {136},

publisher = {Frontiers},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bontempi2021,

title = {The CLAIRE COVID-19 initiative: approach, experiences and recommendations},

author = {Gianluca Bontempi and Ricardo Chavarriaga and Hans De Canck and Emanuela Girardi and Holger Hoos and Iarla Kilbane-Dawe and Tonio Ball and Ann Nowé and Jose Sousa and Davide Bacciu and Marco Aldinucci and Manlio De Domenico and Alessandro Saffiotti and Marco Maratea},

doi = {10.1007/s10676-020-09567-7},

year  = {2021},

date = {2021-02-09},

journal = {Ethics and Information Technology},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Valenti2021,

title = {A Deep Classifier for Upper-Limbs Motor Anticipation Tasks in an Online BCI Setting},

author = {Andrea Valenti, Michele Barsotti, Davide Bacciu and Luca Ascari

},

url = {https://www.mdpi.com/2306-5354/8/2/21, Open Access },

doi = {10.3390/bioengineering8020021},

year  = {2021},

date = {2021-02-05},

urldate = {2021-02-05},

journal = {Bioengineering },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BacciuNCA2020,

title = {Topographic mapping for quality inspection and intelligent filtering of smart-bracelet data},

author = {Davide Bacciu and Gioele Bertoncini and Davide Morelli},

doi = {10.1007/s00521-020-05600-4},

year  = {2021},

date = {2021-01-04},

urldate = {2021-01-04},

journal = {Neural Computing Applications},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{CRECCHI2021,

title = {FADER: Fast Adversarial Example Rejection},

author = {Francesco Crecchi and Marco Melis and Angelo Sotgiu and Davide Bacciu and Battista Biggio},

url = {https://arxiv.org/abs/2010.09119, Arxiv},

doi = {https://doi.org/10.1016/j.neucom.2021.10.082},

issn = {0925-2312},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Neurocomputing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gentleGraphs2020,

title = {A Gentle Introduction to Deep Learning for Graphs},

author = {Davide Bacciu and Federico Errica and Alessio Micheli and Marco Podda},

url = {https://arxiv.org/abs/1912.12693, Arxiv

https://doi.org/10.1016/j.neunet.2020.06.006, Original Paper},

doi = {10.1016/j.neunet.2020.06.006},

year  = {2020},

date = {2020-09-01},

urldate = {2020-09-01},

journal = {Neural Networks},

volume = {129},

pages = {203-221},

publisher = {Elsevier},

abstract = {The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community. The snap increase in the amount and breadth of related research has come at the price of little systematization of knowledge and attention to earlier literature. This work is designed as a tutorial introduction to the field of deep learning for graphs. It favours a consistent and progressive introduction of the main concepts and architectural aspects over an exposition of the most recent literature, for which the reader is referred to available surveys. The paper takes a top-down view to the problem, introducing a generalized formulation of graph representation learning based on a local and iterative approach to structured information processing. It introduces the basic building blocks that can be combined to design novel and effective neural models for graphs. The methodological exposition is complemented by a discussion of interesting research challenges and applications in the field. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}