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08/03/2022
Centroids Matching: an efficient Continual Learning approach operating in the embedding space
Catastrophic forgetting (CF) occurs when a neural network loses the info...
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05/04/2022
Hypercomplex Image-to-Image Translation
Image-to-image translation (I2I) aims at transferring the content repres...
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04/04/2022
Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representation
Spatial audio methods are gaining a growing interest due to the spread o...
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02/21/2022
L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environment
The L3DAS22 Challenge is aimed at encouraging the development of machine...
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02/11/2022
Continual Learning with Invertible Generative Models
Catastrophic forgetting (CF) happens whenever a neural network overwrite...
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02/04/2022
Pixle: a fast and effective black-box attack based on rearranging pixels
Recent research has found that neural networks are vulnerable to several...
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09/20/2021
A Meta-Learning Approach for Training Explainable Graph Neural Networks
In this paper, we investigate the degree of explainability of graph neur...
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05/06/2021
Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble Methods
In this paper, we propose a novel ensembling technique for deep neural n...
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04/29/2021
Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning
Graph representation learning has become a ubiquitous component in many ...
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04/19/2021
A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling
Nonlinear models are known to provide excellent performance in real-worl...
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04/12/2021
L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing
The L3DAS21 Challenge is aimed at encouraging and fostering collaborativ...
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10/22/2020
Quaternion-Valued Variational Autoencoder
Deep probabilistic generative models have achieved incredible success in...
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07/05/2020
Pseudo-Rehearsal for Continual Learning with Normalizing Flows
Catastrophic forgetting (CF) happens whenever a neural network overwrite...
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04/27/2020
Why should we add early exits to neural networks?
Deep neural networks are generally designed as a stack of differentiable...
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03/02/2020
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
Bayesian Neural Networks (BNNs) are trained to optimize an entire distri...
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02/24/2020
Adaptive Propagation Graph Convolutional Network
Graph convolutional networks (GCNs) are a family of neural network model...
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09/09/2019
Efficient Continual Learning in Neural Networks with Embedding Regularization
Continual learning of deep neural networks is a key requirement for scal...
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08/08/2019
A Multimodal Deep Network for the Reconstruction of T2W MR Images
Multiple sclerosis is one of the most common chronic neurological diseas...
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07/26/2019
Compressing deep quaternion neural networks with targeted regularization
In recent years, hyper-complex deep networks (e.g., quaternion-based) ha...
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06/20/2019
Efficient data augmentation using graph imputation neural networks
Recently, data augmentation in the semi-supervised regime, where unlabel...
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05/06/2019
Missing Data Imputation with Adversarially-trained Graph Convolutional Networks
Missing data imputation (MDI) is a fundamental problem in many scientifi...
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03/28/2019
On the Stability and Generalization of Learning with Kernel Activation Functions
In this brief we investigate the generalization properties of a recently...
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02/06/2019
Widely Linear Kernels for Complex-Valued Kernel Activation Functions
Complex-valued neural networks (CVNNs) have been shown to be powerful no...
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12/17/2018
Quaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events
Learning from data in the quaternion domain enables us to exploit intern...
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07/11/2018
Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions
Gated recurrent neural networks have achieved remarkable results in the ...
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02/26/2018
Improving Graph Convolutional Networks with Non-Parametric Activation Functions
Graph neural networks (GNNs) are a class of neural networks that allow t...
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02/22/2018
Complex-valued Neural Networks with Non-parametric Activation Functions
Complex-valued neural networks (CVNNs) are a powerful modeling tool for ...
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07/13/2017
Kafnets: kernel-based non-parametric activation functions for neural networks
Neural networks are generally built by interleaving (adaptable) linear l...
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07/02/2016
Group Sparse Regularization for Deep Neural Networks
In this paper, we consider the joint task of simultaneously optimizing (...
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05/18/2016