Search Results for author: Aurelio Uncini

Found 35 papers, 20 papers with code

Overview of the L3DAS23 Challenge on Audio-Visual Extended Reality

no code implementations14 Feb 2024 Christian Marinoni, Riccardo Fosco Gramaccioni, Changan Chen, Aurelio Uncini, Danilo Comminiello

The primary goal of the L3DAS23 Signal Processing Grand Challenge at ICASSP 2023 is to promote and support collaborative research on machine learning for 3D audio signal processing, with a specific emphasis on 3D speech enhancement and 3D Sound Event Localization and Detection in Extended Reality applications.

Audio Signal Processing Sound Event Localization and Detection +1

Generalizing Medical Image Representations via Quaternion Wavelet Networks

1 code implementation16 Oct 2023 Luigi Sigillo, Eleonora Grassucci, Aurelio Uncini, Danilo Comminiello

The proposed quaternion wavelet network (QUAVE) can be easily integrated with any pre-existing medical image analysis or synthesis task, and it can be involved with real, quaternion, or hypercomplex-valued models, generalizing their adoption to single-channel data.

PHYDI: Initializing Parameterized Hypercomplex Neural Networks as Identity Functions

1 code implementation11 Oct 2023 Matteo Mancanelli, Eleonora Grassucci, Aurelio Uncini, Danilo Comminiello

Neural models based on hypercomplex algebra systems are growing and prolificating for a plethora of applications, ranging from computer vision to natural language processing.

Centroids Matching: an efficient Continual Learning approach operating in the embedding space

1 code implementation3 Aug 2022 Jary Pomponi, Simone Scardapane, Aurelio Uncini

In this paper, we propose a novel regularization method called Centroids Matching, that, inspired by meta-learning approaches, fights CF by operating in the feature space produced by the neural network, achieving good results while requiring a small memory footprint.

Continual Learning Incremental Learning +1

Hypercomplex Image-to-Image Translation

1 code implementation4 May 2022 Eleonora Grassucci, Luigi Sigillo, Aurelio Uncini, Danilo Comminiello

Image-to-image translation (I2I) aims at transferring the content representation from an input domain to an output one, bouncing along different target domains.

Image-to-Image Translation Translation

Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representation

1 code implementation4 Apr 2022 Eleonora Grassucci, Gioia Mancini, Christian Brignone, Aurelio Uncini, Danilo Comminiello

We show that our dual quaternion SELD model with temporal convolution blocks (DualQSELD-TCN) achieves better results with respect to real and quaternion-valued baselines thanks to our augmented representation of the sound field.

Sound Event Localization and Detection

L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environment

1 code implementation21 Feb 2022 Eric Guizzo, Christian Marinoni, Marco Pennese, Xinlei Ren, Xiguang Zheng, Chen Zhang, Bruno Masiero, Aurelio Uncini, Danilo Comminiello

The L3DAS22 Challenge is aimed at encouraging the development of machine learning strategies for 3D speech enhancement and 3D sound localization and detection in office-like environments.

Sound Event Localization and Detection Speech Enhancement

Continual Learning with Invertible Generative Models

1 code implementation11 Feb 2022 Jary Pomponi, Simone Scardapane, Aurelio Uncini

We show that our method performs favorably with respect to state-of-the-art approaches in the literature, with bounded computational power and memory overheads.

Continual Learning

Pixle: a fast and effective black-box attack based on rearranging pixels

1 code implementation4 Feb 2022 Jary Pomponi, Simone Scardapane, Aurelio Uncini

Recent research has found that neural networks are vulnerable to several types of adversarial attacks, where the input samples are modified in such a way that the model produces a wrong prediction that misclassifies the adversarial sample.

A Meta-Learning Approach for Training Explainable Graph Neural Networks

1 code implementation20 Sep 2021 Indro Spinelli, Simone Scardapane, Aurelio Uncini

Experiments on synthetic and real-world datasets for node and graph classification show that we can produce models that are consistently easier to explain by different algorithms.

Graph Classification Meta-Learning +1

Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble Methods

2 code implementations6 May 2021 Jary Pomponi, Simone Scardapane, Aurelio Uncini

In this paper, we propose a novel ensembling technique for deep neural networks, which is able to drastically reduce the required memory compared to alternative approaches.

Continual Learning

FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning

no code implementations29 Apr 2021 Indro Spinelli, Simone Scardapane, Amir Hussain, Aurelio Uncini

Furthermore, to better evaluate the gains, we propose a new dyadic group definition to measure the bias of a link prediction task when paired with group-based fairness metrics.

Fairness Graph Representation Learning +1

A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling

no code implementations19 Apr 2021 Danilo Comminiello, Alireza Nezamdoust, Simone Scardapane, Michele Scarpiniti, Amir Hussain, Aurelio Uncini

In order to make this class of functional link adaptive filters (FLAFs) efficient, we propose low-complexity expansions and frequency-domain adaptation of the parameters.

Acoustic echo cancellation Domain Adaptation

L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing

1 code implementation12 Apr 2021 Eric Guizzo, Riccardo F. Gramaccioni, Saeid Jamili, Christian Marinoni, Edoardo Massaro, Claudia Medaglia, Giuseppe Nachira, Leonardo Nucciarelli, Ludovica Paglialunga, Marco Pennese, Sveva Pepe, Enrico Rocchi, Aurelio Uncini, Danilo Comminiello

The L3DAS21 Challenge is aimed at encouraging and fostering collaborative research on machine learning for 3D audio signal processing, with particular focus on 3D speech enhancement (SE) and 3D sound localization and detection (SELD).

Audio Signal Processing BIG-bench Machine Learning +1

A Quaternion-Valued Variational Autoencoder

3 code implementations22 Oct 2020 Eleonora Grassucci, Danilo Comminiello, Aurelio Uncini

Deep probabilistic generative models have achieved incredible success in many fields of application.

Combined Sparse Regularization for Nonlinear Adaptive Filters

no code implementations24 Jul 2020 Danilo Comminiello, Michele Scarpiniti, Simone Scardapane, Luis A. Azpicueta-Ruiz, Aurelio Uncini

Nonlinear adaptive filters often show some sparse behavior due to the fact that not all the coefficients are equally useful for the modeling of any nonlinearity.

Pseudo-Rehearsal for Continual Learning with Normalizing Flows

1 code implementation ICML Workshop LifelongML 2020 Jary Pomponi, Simone Scardapane, Aurelio Uncini

We show that our method performs favorably with respect to state-of-the-art approaches in the literature, with bounded computational power and memory overheads.

Continual Learning

Why should we add early exits to neural networks?

no code implementations27 Apr 2020 Simone Scardapane, Michele Scarpiniti, Enzo Baccarelli, Aurelio Uncini

Deep neural networks are generally designed as a stack of differentiable layers, in which a prediction is obtained only after running the full stack.

Bayesian Neural Networks With Maximum Mean Discrepancy Regularization

4 code implementations2 Mar 2020 Jary Pomponi, Simone Scardapane, Aurelio Uncini

Bayesian Neural Networks (BNNs) are trained to optimize an entire distribution over their weights instead of a single set, having significant advantages in terms of, e. g., interpretability, multi-task learning, and calibration.

Image Classification Multi-Task Learning +1

Adaptive Propagation Graph Convolutional Network

1 code implementation24 Feb 2020 Indro Spinelli, Simone Scardapane, Aurelio Uncini

Graph convolutional networks (GCNs) are a family of neural network models that perform inference on graph data by interleaving vertex-wise operations and message-passing exchanges across nodes.

Efficient Continual Learning in Neural Networks with Embedding Regularization

1 code implementation9 Sep 2019 Jary Pomponi, Simone Scardapane, Vincenzo Lomonaco, Aurelio Uncini

Continual learning of deep neural networks is a key requirement for scaling them up to more complex applicative scenarios and for achieving real lifelong learning of these architectures.

Continual Learning

A Multimodal Deep Network for the Reconstruction of T2W MR Images

no code implementations8 Aug 2019 Antonio Falvo, Danilo Comminiello, Simone Scardapane, Michele Scarpiniti, Aurelio Uncini

In this paper, we present a deep learning method that is able to reconstruct subsampled MR images obtained by reducing the k-space data, while maintaining a high image quality that can be used to observe brain lesions.

Compressing deep quaternion neural networks with targeted regularization

no code implementations26 Jul 2019 Riccardo Vecchi, Simone Scardapane, Danilo Comminiello, Aurelio Uncini

To this end, we investigate two extensions of l1 and structured regularization to the quaternion domain.

Image Reconstruction

Efficient data augmentation using graph imputation neural networks

no code implementations20 Jun 2019 Indro Spinelli, Simone Scardapane, Michele Scarpiniti, Aurelio Uncini

Recently, data augmentation in the semi-supervised regime, where unlabeled data vastly outnumbers labeled data, has received a considerable attention.

Data Augmentation Imputation

Missing Data Imputation with Adversarially-trained Graph Convolutional Networks

1 code implementation6 May 2019 Indro Spinelli, Simone Scardapane, Aurelio Uncini

We also explore a few extensions to the basic architecture involving the use of residual connections between layers, and of global statistics computed from the data set to improve the accuracy.

Denoising Imputation

On the Stability and Generalization of Learning with Kernel Activation Functions

no code implementations28 Mar 2019 Michele Cirillo, Simone Scardapane, Steven Van Vaerenbergh, Aurelio Uncini

In this brief we investigate the generalization properties of a recently-proposed class of non-parametric activation functions, the kernel activation functions (KAFs).

Widely Linear Kernels for Complex-Valued Kernel Activation Functions

no code implementations6 Feb 2019 Simone Scardapane, Steven Van Vaerenbergh, Danilo Comminiello, Aurelio Uncini

Complex-valued neural networks (CVNNs) have been shown to be powerful nonlinear approximators when the input data can be properly modeled in the complex domain.

Image Classification Test

Quaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events

no code implementations17 Dec 2018 Danilo Comminiello, Marco Lella, Simone Scardapane, Aurelio Uncini

Learning from data in the quaternion domain enables us to exploit internal dependencies of 4D signals and treating them as a single entity.

Event Detection Sound Event Detection

Improving Graph Convolutional Networks with Non-Parametric Activation Functions

no code implementations26 Feb 2018 Simone Scardapane, Steven Van Vaerenbergh, Danilo Comminiello, Aurelio Uncini

Graph neural networks (GNNs) are a class of neural networks that allow to efficiently perform inference on data that is associated to a graph structure, such as, e. g., citation networks or knowledge graphs.

Knowledge Graphs

Complex-valued Neural Networks with Non-parametric Activation Functions

2 code implementations22 Feb 2018 Simone Scardapane, Steven Van Vaerenbergh, Amir Hussain, Aurelio Uncini

Complex-valued neural networks (CVNNs) are a powerful modeling tool for domains where data can be naturally interpreted in terms of complex numbers.

Kafnets: kernel-based non-parametric activation functions for neural networks

2 code implementations13 Jul 2017 Simone Scardapane, Steven Van Vaerenbergh, Simone Totaro, Aurelio Uncini

Neural networks are generally built by interleaving (adaptable) linear layers with (fixed) nonlinear activation functions.

Group Sparse Regularization for Deep Neural Networks

1 code implementation2 Jul 2016 Simone Scardapane, Danilo Comminiello, Amir Hussain, Aurelio Uncini

In this paper, we consider the joint task of simultaneously optimizing (i) the weights of a deep neural network, (ii) the number of neurons for each hidden layer, and (iii) the subset of active input features (i. e., feature selection).

feature selection Handwritten Digit Recognition

Effective Blind Source Separation Based on the Adam Algorithm

no code implementations25 May 2016 Michele Scarpiniti, Simone Scardapane, Danilo Comminiello, Raffaele Parisi, Aurelio Uncini

In this paper, we derive a modified InfoMax algorithm for the solution of Blind Signal Separation (BSS) problems by using advanced stochastic methods.

blind source separation Stochastic Optimization

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