@conference{carta2021ex,

title = {Ex-Model: Continual Learning from a Stream of Trained Models},

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

url = {https://arxiv.org/pdf/2112.06511.pdf, Arxiv},

year  = {2022},

date = {2022-06-20},

urldate = {2022-06-20},

booktitle = {Proceedings of the CVPR 2022 Workshop on Continual Learning },

journal = {arXiv preprint arXiv:2112.06511},

pages = {3790-3799},

organization = {IEEE},

abstract = {Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt, and generalize continually in an efficient, effective, and scalable way is fundamental for a sustainable development of Artificial Intelligent systems. However, an agent-centric view of continual learning requires learning directly from raw data, which limits the interaction between independent agents, the efficiency, and the privacy of current approaches. Instead, we argue that continual learning systems should exploit the availability of compressed information in the form of trained models. In this paper, we introduce and formalize a new paradigm named "Ex-Model Continual Learning" (ExML), where an agent learns from a sequence of previously trained models instead of raw data. We further contribute with three ex-model continual learning algorithms and an empirical setting comprising three datasets (MNIST, CIFAR-10 and CORe50), and eight scenarios, where the proposed algorithms are extensively tested. Finally, we highlight the peculiarities of the ex-model paradigm and we point out interesting future research directions. },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{decaro2022aiasaservice,

title = {AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving},

author = {Valerio De Caro and Saira Bano and Achilles Machumilane and Alberto Gotta and Pietro Cassará and Antonio Carta and Christos Sardianos and Christos Chronis and Iraklis Varlamis and Konstantinos Tserpes and Vincenzo Lomonaco and Claudio Gallicchio and Davide Bacciu},

url = {https://arxiv.org/pdf/2202.01645.pdf, arxiv},

year  = {2022},

date = {2022-03-21},

urldate = {2022-03-21},

booktitle = {Proceedings of the 20th International Conference on Pervasive Computing and Communications (PerCom 2022)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Bacciu2021c,

title = { Deep learning for graphs},

author = {Davide Bacciu and Filippo Maria Bianchi and Benjamin Paassen and Cesare Alippi},

editor = {Michel Verleysen},

doi = {10.14428/esann/2021.ES2021-5},

year  = {2021},

date = {2021-10-06},

urldate = {2021-10-06},

booktitle = {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning  (ESANN 2021)},

pages = {89-98},

abstract = { Deep learning for graphs encompasses all those models endowed with multiple layers of abstraction, which operate on data represented as graphs. The most common building blocks of these models are graph encoding layers, which compute a vector embedding for each node in a graph based on a sum of messages received from its neighbors. However, the family also includes architectures with decoders from vectors to graphs and models that process time-varying graphs and hypergraphs. In this paper, we provide an overview of the key concepts in the field, point towards open questions, and frame the contributions of the ESANN 2021 special session into the broader context of deep learning for graphs. },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Valenti2021b,

title = {Calliope - A Polyphonic Music Transformer},

author = {Andrea Valenti and Stefano Berti and Davide Bacciu},

editor = {Michel Verleysen},

url = { The polyphonic nature of music makes the application of deep learning to music modelling a challenging task. On the other hand, the Transformer architecture seems to be a good fit for this kind of data. In this work, we present Calliope, a novel autoencoder model based on Transformers for the efficient modelling of multi-track sequences of polyphonic music. The experiments show that our model is able to improve the state of the art on musical sequence reconstruction and generation, with remarkably good results especially on long sequences. },

doi = {10.14428/esann/2021.ES2021-63},

year  = {2021},

date = {2021-10-06},

urldate = {2021-10-06},

booktitle = {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning  (ESANN 2021)},

pages = {405-410},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{BacciuPoddaIJCNN2021,

title = {GraphGen-Redux: a Fast and Lightweight Recurrent Model for Labeled Graph Generation},

author = {Davide Bacciu and Marco Podda},

doi = {10.1109/IJCNN52387.2021.9533743},

year  = {2021},

date = {2021-07-18},

urldate = {2021-07-18},

booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021)},

organization = {IEEE},

abstract = {The problem of labeled graph generation is gaining attention in the Deep Learning community. The task is challenging due to the sparse and discrete nature of graph spaces. Several approaches have been proposed in the literature, most of which require to transform the graphs into sequences that encode their structure and labels and to learn the distribution of such sequences through an auto-regressive generative model. Among this family of approaches, we focus on the Graphgen model. The preprocessing phase of Graphgen transforms graphs into unique edge sequences called Depth-First Search (DFS) codes, such that two isomorphic graphs are assigned the same DFS code. Each element of a DFS code is associated with a graph edge: specifically, it is a quintuple comprising one node identifier for each of the two endpoints, their node labels, and the edge label. Graphgen learns to generate such sequences auto-regressively and models the probability of each component of the quintuple independently. While effective, the independence assumption made by the model is too loose to capture the complex label dependencies of real-world graphs precisely. By introducing a novel graph preprocessing approach, we are able to process the labeling information of both nodes and edges jointly. The corresponding model, which we term Graphgen-redux, improves upon the generative performances of Graphgen in a wide range of datasets of chemical and social graphs. In addition, it uses approximately 78% fewer parameters than the vanilla variant and requires 50% fewer epochs of training on average.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{smc2020,

title = {ROS-Neuro Integration of Deep Convolutional Autoencoders for EEG Signal Compression in Real-time BCIs},

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

url = {https://arxiv.org/abs/2008.13485, Arxiv},

year  = {2020},

date = {2020-10-11},

urldate = {2020-10-11},

booktitle = {Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},

publisher = {IEEE},

abstract = {     Typical EEG-based BCI applications require the computation of complex functions over the noisy EEG channels to be carried out in an efficient way. Deep learning algorithms are capable of learning flexible nonlinear functions directly from data, and their constant processing latency is perfect for their deployment into online BCI systems. However, it is crucial for the jitter of the processing system to be as low as possible, in order to avoid unpredictable behaviour that can ruin the system's overall usability. In this paper, we present a novel encoding method, based on on deep convolutional autoencoders, that is able to perform efficient compression of the raw EEG inputs. We deploy our model in a ROS-Neuro node, thus making it suitable for the integration in ROS-based BCI and robotic systems in real world scenarios. The experimental results show that our system is capable to generate meaningful compressed encoding preserving to original information contained in the raw input. They also show that the ROS-Neuro node is able to produce such encodings at a steady rate, with minimal jitter. We believe that our system can represent an important step towards the development of an effective BCI processing pipeline fully standardized in ROS-Neuro framework. },

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{Wcci20CL,

title = {Continual Learning with Gated Incremental Memories for Sequential Data Processing},

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

url = {https://arxiv.org/pdf/2004.04077.pdf, Arxiv},

doi = {10.1109/IJCNN48605.2020.9207550},

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{ecai2020,

title = { Learning a Latent Space of Style-Aware Music Representations by Adversarial Autoencoders},

author = {Andrea Valenti and Antonio Carta and Davide Bacciu},

url = {https://arxiv.org/abs/2001.05494},

year  = {2020},

date = {2020-06-08},

booktitle = {Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{ecml2020LMN,

title = {Incremental training of a recurrent neural network exploiting a multi-scale dynamic memory},

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

year  = {2020},

date = {2020-06-05},

urldate = {2020-06-05},

booktitle = {Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2020 (ECML-PKDD 2020)},

publisher = {Springer International Publishing},

abstract = {The effectiveness of recurrent neural networks can be largely influenced by their ability to store into their dynamical memory information extracted from input sequences at different frequencies and timescales. Such a feature can be introduced into a neural architecture by an appropriate modularization of the dynamic memory. In this paper we propose a novel incrementally trained recurrent architecture targeting explicitly multi-scale learning. First, we show how to extend the architecture of a simple RNN by separating its hidden state into different modules, each subsampling the network hidden activations at different frequencies. Then, we discuss a training algorithm where new modules are iteratively added to the model to learn progressively longer dependencies. Each new module works at a slower frequency than the previous ones and it is initialized to encode the subsampled sequence of hidden activations. Experimental results on synthetic and real-world datasets on speech recognition and handwritten characters show that the modular architecture and the incremental training algorithm improve the ability of recurrent neural networks to capture long-term dependencies.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{iclr19,

title = {A Fair Comparison of Graph Neural Networks for Graph Classification},

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

url = {https://openreview.net/pdf?id=HygDF6NFPB, PDF

https://iclr.cc/virtual_2020/poster_HygDF6NFPB.html, Talk

https://github.com/diningphil/gnn-comparison, Code},

year  = {2020},

date = {2020-04-30},

booktitle = {Proceedings of the Eighth International Conference on Learning Representations (ICLR 2020)},

abstract = {Experimental reproducibility and replicability are critical topics in machine learning. Authors have often raised concerns about their lack in scientific publications to improve the quality of the field. Recently, the graph representation learning field has attracted the attention of a wide research community, which resulted in a large stream of works.

As such, several Graph Neural Network models have been developed to effectively tackle graph classification. However, experimental procedures often lack rigorousness and are hardly reproducible. Motivated by this, we provide an overview of common practices that should be avoided to fairly compare with the state of the art. To counter this troubling trend, we ran more than 47000 experiments in a controlled and uniform framework to re-evaluate five popular models across nine common benchmarks. Moreover, by comparing GNNs with structure-agnostic baselines we provide convincing evidence that, on some datasets, structural information has not been exploited yet. We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{esann20Tutorial,

title = {Tensor Decompositions in Deep Learning},

author = {Davide Bacciu and Danilo Mandic},

editor = {Michel Verleysen},

url = {https://arxiv.org/abs/2002.11835},

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}

}

@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}

}

@conference{ssci19,

title = {Sequential Sentence Embeddings for Semantic Similarity},

author = {Davide Bacciu and Antonio Carta},

doi = {10.1109/SSCI44817.2019.9002824},

year  = {2019},

date = {2019-12-06},

urldate = {2019-12-06},

booktitle = {Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI'19)},

publisher = {IEEE},

abstract = {  Sentence embeddings are distributed representations of sentences intended to be general features to be effectively used as input for deep learning models across different natural language processing tasks.

  State-of-the-art sentence embeddings for semantic similarity are computed with a weighted average of pretrained word embeddings, hence completely ignoring the contribution of word ordering within a sentence in defining its semantics. We propose a novel approach to compute sentence embeddings for semantic similarity that exploits a linear autoencoder for sequences. The method can be trained in closed form and it is easy to fit on unlabeled sentences. Our method provides a grounded approach to identify and subtract common discourse from a sentence and its embedding, to remove associated uninformative features. Unlike similar methods in the literature (e.g. the popular Smooth Inverse Frequency approach), our method is able to account for word order. We show that our estimate of the common discourse vector improves the results on two different semantic similarity benchmarks when compared to related approaches from the literature.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{aiia2019,

title = {A non-negative factorization approach to node pooling in graph convolutional neural networks},

author = {Davide Bacciu and Luigi {Di Sotto}},

url = {https://arxiv.org/pdf/1909.03287.pdf},

year  = {2019},

date = {2019-11-22},

booktitle = {Proceedings of the 18th International Conference of the Italian Association for Artificial Intelligence (AIIA 2019)},

publisher = {Springer-Verlag},

series = {Lecture Notes in Artificial Intelligence},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{lmnArx18,

title = {Linear Memory Networks},

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

url = {https://arxiv.org/pdf/1811.03356.pdf},

doi = {10.1007/978-3-030-30487-4_40},

year  = {2019},

date = {2019-09-17},

urldate = {2019-09-17},

booktitle = {Proceedings of the 28th International Conference on Artificial Neural Networks (ICANN 2019), },

volume = {11727},

pages = {513-525 },

publisher = {Springer-Verlag},

series = {Lecture Notes in Computer Science},

abstract = {Recurrent neural networks can learn complex transduction problems that require maintaining and actively exploiting a memory of their inputs. Such models traditionally consider memory and input-output functionalities indissolubly entangled. We introduce a novel recurrent architecture based on the conceptual separation between the functional input-output transformation and the memory mechanism, showing how they can be implemented through different neural components. By building on such conceptualization, we introduce the Linear Memory Network, a recurrent model comprising a feedforward neural network, realizing the non-linear functional transformation, and a linear autoencoder for sequences, implementing the memory component. The resulting architecture can be efficiently trained by building on closed-form solutions to linear optimization problems. Further, by exploiting equivalence results between feedforward and recurrent neural networks we devise a pretraining schema for the proposed architecture. Experiments on polyphonic music datasets show competitive results against gated recurrent networks and other state of the art models. },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{esann19Attacks,

title = {Detecting Black-box Adversarial Examples through Nonlinear Dimensionality Reduction},

author = {Francesco Crecchi and Davide Bacciu and Battista Biggio },

editor = {Michel Verleysen},

url = {https://arxiv.org/pdf/1904.13094.pdf},

year  = {2019},

date = {2019-04-24},

booktitle = {Proceedings  of the European Symposium on Artificial Neural Networks,  Computational Intelligence and Machine Learning (ESANN'19)},

publisher = {i6doc.com},

address = {Louvain-la-Neuve, Belgium},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{esann19Tutorial,

title = {Societal Issues in Machine Learning: When Learning from Data is Not Enough},

author = { Davide Bacciu and Battista Biggio and Francesco Crecchi and Paulo J. G. Lisboa and José D. Martin and Luca Oneto and Alfredo Vellido},

editor = {Michel Verleysen},

url = {https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-6.pdf},

year  = {2019},

date = {2019-04-24},

booktitle = {Proceedings  of the European Symposium on Artificial Neural Networks,  Computational Intelligence and Machine Learning (ESANN'19)},

publisher = {i6doc.com},

address = {Louvain-la-Neuve, Belgium},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@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}

}

@conference{icml2018,

title = {Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing},

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

url = {https://arxiv.org/abs/1805.10636},

year  = {2018},

date = {2018-07-11},

urldate = {2018-07-11},

booktitle = {Proceedings of the 35th International Conference on Machine Learning (ICML 2018)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{ijcnn2018,

title = {Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs},

author = {Bacciu Davide and Bongiorno Andrea},

url = {https://arxiv.org/abs/1805.09244},

year  = {2018},

date = {2018-07-09},

urldate = {2018-07-09},

booktitle = {Proceedings  of the 2018 International Joint Conference on Neural Networks (IJCNN 2018) },

pages = {1-9},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{esann2018Tut,

title = {Bioinformatics and medicine in the era of deep learning},

author = {Bacciu Davide and Lisboa Paulo JG and Martin Jose D and Stoean Ruxandra and Vellido Alfredo},

editor = {Michel Verleysen},

url = {http://arxiv.org/abs/1802.09791},

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 = {345-354},

publisher = {i6doc.com},

address = {Louvain-la-Neuve, Belgium},

abstract = {Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{dl2017,

title = {Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data},

author = {Bacciu Davide},

url = {https://arxiv.org/abs/1711.07784},

year  = {2017},

date = {2017-11-27},

urldate = {2017-11-27},

booktitle = {Proc. of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI'17)},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{ijcnn2017,

title = {DropIn: Making Neural Networks Robust to Missing Inputs by Dropout},

author = {Bacciu Davide and Crecchi Francesco and Morelli Davide},

url = {https://arxiv.org/abs/1705.02643},

doi = {10.1109/IJCNN.2017.7966106},

isbn = {978-1-5090-6182-2},

year  = {2017},

date = {2017-05-19},

urldate = {2017-05-19},

booktitle = {Proceedings  of the 2017 International Joint Conference on Neural Networks (IJCNN 2017) },

pages = {2080-2087},

publisher = {IEEE},

abstract = {The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at prediction time. By building on the ensembling properties of Dropout regularization, we propose a methodology, named DropIn, which efficiently trains a neural model as a committee machine of subnetworks, each capable of predicting with a subset of the original input features. We discuss the application of the DropIn methodology in the context of Reservoir Computing models and targeting applications characterized by input sources that are unreliable or prone to be disconnected, such as in pervasive wireless sensor networks and ambient intelligence. We provide an experimental assessment using real-world data from such application domains, showing how the Dropin methodology allows to maintain predictive performances comparable to those of a model without missing features, even when 20%–50% of the inputs are not available.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{fun2016,

title = {An Investigation into Cybernetic Humor, or: Can Machines Laugh?},

author = {Bacciu Davide and Gervasi Vincenzo and Prencipe Giuseppe},

editor = {Erik D. Demaine and Fabrizio Grandoni},

url = {http://drops.dagstuhl.de/opus/volltexte/2016/5882},

doi = {10.4230/LIPIcs.FUN.2016.3},

issn = {1868-8969},

year  = {2016},

date = {2016-06-10},

booktitle = {Proceedings of  the 8th International Conference on Fun with Algorithms (FUN'16) },

volume = {49},

pages = {1-15},

publisher = {Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik},

series = {Leibniz International Proceedings in Informatics (LIPIcs)},

abstract = {The mechanisms of humour have been the subject of much study and investigation, starting with and up to our days. Much of this work is based on literary theories, put forward by some of the most eminent philosophers and thinkers of all times, or medical theories, investigating the impact of humor on brain activity or behaviour. Recent functional neuroimaging studies, for instance, have investigated the process of comprehending and appreciating humor by examining functional activity in distinctive regions of brains stimulated by joke corpora. Yet, there is precious little work on the computational side, possibly due to the less hilarious nature of computer scientists as compared to men of letters and sawbones. In this paper, we set to investigate whether literary theories of humour can stand the test of algorithmic laughter. Or, in other words, we ask ourselves the vexed question: Can machines laugh? We attempt to answer that question by testing whether an algorithm - namely, a neural network - can "understand" humour, and in particular whether it is possible to automatically identify abstractions that are predicted to be relevant by established literary theories about the mechanisms of humor. Notice that we do not focus here on distinguishing humorous from serious statements - a feat that is clearly way beyond the capabilities of the average human voter, not to mention the average machine - but rather on identifying the underlying mechanisms and triggers that are postulated to exist by literary theories, by verifying if similar mechanisms can be learned by machines. },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}