Search Results for author: Davide Bacciu

Found 47 papers, 24 papers with code

Inductive learning for product assortment graph completion

no code implementations4 Oct 2021 Haris Dukic, Georgios Deligiorgis, Pierpaolo Sepe, Davide Bacciu, Marco Trincavelli

Global retailers have assortments that contain hundreds of thousands of products that can be linked by several types of relationships like style compatibility, "bought together", "watched together", etc.

GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation

1 code implementation18 Jul 2021 Marco Podda, Davide Bacciu

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.

Graph Generation

Calliope -- A Polyphonic Music Transformer

no code implementations8 Jul 2021 Andrea Valenti, Stefano Berti, Davide Bacciu

The polyphonic nature of music makes the application of deep learning to music modelling a challenging task.

Continual Learning with Echo State Networks

1 code implementation17 May 2021 Andrea Cossu, Davide Bacciu, Antonio Carta, Claudio Gallicchio, Vincenzo Lomonaco

Continual Learning (CL) refers to a learning setup where data is non stationary and the model has to learn without forgetting existing knowledge.

Continual Learning

A causal learning framework for the analysis and interpretation of COVID-19 clinical data

no code implementations14 May 2021 Elisa Ferrari, Luna Gargani, Greta Barbieri, Lorenzo Ghiadoni, Francesco Faita, Davide Bacciu

We present a workflow for clinical data analysis that relies on Bayesian Structure Learning (BSL), an unsupervised learning approach, robust to noise and biases, that allows to incorporate prior medical knowledge into the learning process and that provides explainable results in the form of a graph showing the causal connections among the analyzed features.

Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-loss

1 code implementation13 May 2021 Elisa Ferrari, Davide Bacciu

Resilience to class imbalance and confounding biases, together with the assurance of fairness guarantees are highly desirable properties of autonomous decision-making systems with real-life impact.

Decision Making Fairness

MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks

1 code implementation16 Apr 2021 Danilo Numeroso, Davide Bacciu

Explainable AI (XAI) is a research area whose objective is to increase trustworthiness and to enlighten the hidden mechanism of opaque machine learning techniques.

Distilled Replay: Overcoming Forgetting through Synthetic Samples

1 code implementation29 Mar 2021 Andrea Rosasco, Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu

Replay strategies are Continual Learning techniques which mitigate catastrophic forgetting by keeping a buffer of patterns from previous experiences, which are interleaved with new data during training.

Continual Learning

Continual Learning for Recurrent Neural Networks: an Empirical Evaluation

no code implementations12 Mar 2021 Andrea Cossu, Antonio Carta, Vincenzo Lomonaco, Davide Bacciu

We propose two new benchmarks for CL with sequential data based on existing datasets, whose characteristics resemble real-world applications.

Continual Learning

Graph Mixture Density Networks

1 code implementation5 Dec 2020 Federico Errica, Davide Bacciu, Alessio Micheli

We introduce the Graph Mixture Density Networks, a new family of machine learning models that can fit multimodal output distributions conditioned on graphs of arbitrary topology.

Density Estimation Graph Representation Learning

Explaining Deep Graph Networks with Molecular Counterfactuals

1 code implementation9 Nov 2020 Danilo Numeroso, Davide Bacciu

We present a novel approach to tackle explainability of deep graph networks in the context of molecule property prediction tasks, named MEG (Molecular Explanation Generator).

Learning from Non-Binary Constituency Trees via Tensor Decomposition

1 code implementation COLING 2020 Daniele Castellana, Davide Bacciu

Finally, we introduce a Tree-LSTM model which takes advantage of this composition function and we experimentally assess its performance on different NLP tasks.

Tensor Decomposition

Generative Tomography Reconstruction

no code implementations26 Oct 2020 Matteo Ronchetti, Davide Bacciu

We propose an end-to-end differentiable architecture for tomography reconstruction that directly maps a noisy sinogram into a denoised reconstruction.

FADER: Fast Adversarial Example Rejection

no code implementations18 Oct 2020 Francesco Crecchi, Marco Melis, Angelo Sotgiu, Davide Bacciu, Battista Biggio

As a second main contribution of this work, we introduce FADER, a novel technique for speeding up detection-based methods.

Perplexity-free Parametric t-SNE

1 code implementation3 Oct 2020 Francesco Crecchi, Cyril de Bodt, Michel Verleysen, John A. Lee, Davide Bacciu

The t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm is a ubiquitously employed dimensionality reduction (DR) method.

Dimensionality Reduction

ROS-Neuro Integration of Deep Convolutional Autoencoders for EEG Signal Compression in Real-time BCIs

no code implementations31 Aug 2020 Andrea Valenti, Michele Barsotti, Raffaello Brondi, Davide Bacciu, Luca Ascari

Typical EEG-based BCI applications require the computation of complex functions over the noisy EEG channels to be carried out in an efficient way.

EEG

Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory

1 code implementation29 Jun 2020 Antonio Carta, Alessandro Sperduti, Davide Bacciu

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.

Speech Recognition

Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data

1 code implementation18 Jun 2020 Daniele Castellana, Davide Bacciu

The paper introduces two new aggregation functions to encode structural knowledge from tree-structured data.

General Classification

Generalising Recursive Neural Models by Tensor Decomposition

1 code implementation17 Jun 2020 Daniele Castellana, Davide Bacciu

This approximation allows limiting the parameters space size, decoupling it from its strict relation with the size of the hidden encoding space.

Tensor Decomposition

Continual Learning with Gated Incremental Memories for sequential data processing

1 code implementation8 Apr 2020 Andrea Cossu, Antonio Carta, Davide Bacciu

The ability to learn in dynamic, nonstationary environments without forgetting previous knowledge, also known as Continual Learning (CL), is a key enabler for scalable and trustworthy deployments of adaptive solutions.

Continual Learning

Tensor Decompositions in Deep Learning

no code implementations26 Feb 2020 Davide Bacciu, Danilo P. Mandic

The paper surveys the topic of tensor decompositions in modern machine learning applications.

Edge-based sequential graph generation with recurrent neural networks

1 code implementation31 Jan 2020 Davide Bacciu, Alessio Micheli, Marco Podda

Graph generation with Machine Learning is an open problem with applications in various research fields.

Graph Generation

Encoding-based Memory Modules for Recurrent Neural Networks

no code implementations31 Jan 2020 Antonio Carta, Alessandro Sperduti, Davide Bacciu

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.

Theoretically Expressive and Edge-aware Graph Learning

no code implementations24 Jan 2020 Federico Errica, Davide Bacciu, Alessio Micheli

We propose a new Graph Neural Network that combines recent advancements in the field.

Graph Learning

Learning Style-Aware Symbolic Music Representations by Adversarial Autoencoders

1 code implementation15 Jan 2020 Andrea Valenti, Antonio Carta, Davide Bacciu

Through the paper, we show how Gaussian mixtures taking into account music metadata information can be used as an effective prior for the autoencoder latent space, introducing the first Music Adversarial Autoencoder (MusAE).

Music Modeling

A Gentle Introduction to Deep Learning for Graphs

2 code implementations29 Dec 2019 Davide Bacciu, Federico Errica, Alessio Micheli, Marco Podda

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.

Graph Representation Learning

A Fair Comparison of Graph Neural Networks for Graph Classification

1 code implementation ICLR 2020 Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli

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.

Classification General Classification +3

A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks

no code implementations7 Sep 2019 Davide Bacciu, Luigi Di Sotto

The paper discusses a pooling mechanism to induce subsampling in graph structured data and introduces it as a component of a graph convolutional neural network.

Graph Classification

Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models

no code implementations31 May 2019 Daniele Castellana, Davide Bacciu

Bottom-Up Hidden Tree Markov Model is a highly expressive model for tree-structured data.

Detecting Adversarial Examples through Nonlinear Dimensionality Reduction

1 code implementation30 Apr 2019 Francesco Crecchi, Davide Bacciu, Battista Biggio

Deep neural networks are vulnerable to adversarial examples, i. e., carefully-perturbed inputs aimed to mislead classification.

Density Estimation Dimensionality Reduction +1

Deep Tree Transductions - A Short Survey

no code implementations5 Feb 2019 Davide Bacciu, Antonio Bruno

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.

Linear Memory Networks

no code implementations8 Nov 2018 Davide Bacciu, Antonio Carta, Alessandro Sperduti

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.

Text Summarization as Tree Transduction by Top-Down TreeLSTM

no code implementations24 Sep 2018 Davide Bacciu, Antonio Bruno

Extractive compression is a challenging natural language processing problem.

Sentence Compression

Learning Tree Distributions by Hidden Markov Models

no code implementations31 May 2018 Davide Bacciu, Daniele Castellana

Hidden tree Markov models allow learning distributions for tree structured data while being interpretable as nondeterministic automata.

Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing

1 code implementation ICML 2018 Davide Bacciu, Federico Errica, Alessio Micheli

We introduce the Contextual Graph Markov Model, an approach combining ideas from generative models and neural networks for the processing of graph data.

General Classification

Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs

no code implementations23 May 2018 Davide Bacciu, Andrea Bongiorno

The paper introduces concentric Echo State Network, an approach to design reservoir topologies that tries to bridge the gap between deterministically constructed simple cycle models and deep reservoir computing approaches.

Bioinformatics and Medicine in the Era of Deep Learning

no code implementations27 Feb 2018 Davide Bacciu, Paulo J. G. Lisboa, José D. Martín, Ruxandra Stoean, Alfredo Vellido

Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery.

Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data

no code implementations21 Nov 2017 Davide Bacciu

The paper introduces the Hidden Tree Markov Network (HTN), a neuro-probabilistic hybrid fusing the representation power of generative models for trees with the incremental and discriminative learning capabilities of neural networks.

DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout

1 code implementation7 May 2017 Davide Bacciu, Francesco Crecchi, Davide Morelli

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.

Using a Machine Learning Approach to Implement and Evaluate Product Line Features

no code implementations17 Aug 2015 Davide Bacciu, Stefania Gnesi, Laura Semini

Bike-sharing systems are a means of smart transportation in urban environments with the benefit of a positive impact on urban mobility.

Cannot find the paper you are looking for? You can Submit a new open access paper.