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Found 38 papers, 22 papers with code
A Wavelet Diffusion GAN for Image Super-Resolution
In recent years, diffusion models have emerged as a superior alternative to generative adversarial networks (GANs) for high-fidelity image generation, with wide applications in text-to-image generation, image-to-image translation, and super-resolution.
Hierarchical Hypercomplex Network for Multimodal Emotion Recognition
Then, a hypercomplex fusion module learns inter-modal relations among the embeddings of the different modalities.
Demystifying the Hypercomplex: Inductive Biases in Hypercomplex Deep Learning
Hypercomplex algebras have recently been gaining prominence in the field of deep learning owing to the advantages of their division algebras over real vector spaces and their superior results when dealing with multidimensional signals in real-world 3D and 4D paradigms.
Overview of the L3DAS23 Challenge on Audio-Visual Extended Reality
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
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
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
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.
Hypercomplex Image-to-Image Translation
Image-to-image translation (I2I) aims at transferring the content representation from an input domain to an output one, bouncing along different target domains.
Ranked #3 on
Image-to-Image Translation
on CelebA-HQ
Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representation
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.
Ranked #1 on
Sound Event Localization and Detection
on L3DAS21
L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environment
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.
Continual Learning with Invertible Generative Models
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.
Pixle: a fast and effective black-box attack based on rearranging pixels
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
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.
Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble Methods
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.
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning
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.
A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling
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.
L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing
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).
A Quaternion-Valued Variational Autoencoder
Deep probabilistic generative models have achieved incredible success in many fields of application.
Combined Sparse Regularization for Nonlinear Adaptive Filters
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
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.
Why should we add early exits to neural networks?
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
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.
Adaptive Propagation Graph Convolutional Network
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
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.
A Multimodal Deep Network for the Reconstruction of T2W MR Images
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
To this end, we investigate two extensions of l1 and structured regularization to the quaternion domain.
Efficient data augmentation using graph imputation neural networks
Recently, data augmentation in the semi-supervised regime, where unlabeled data vastly outnumbers labeled data, has received a considerable attention.
Missing Data Imputation with Adversarially-trained Graph Convolutional Networks
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.
On the Stability and Generalization of Learning with Kernel Activation Functions
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
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.
Quaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events
Learning from data in the quaternion domain enables us to exploit internal dependencies of 4D signals and treating them as a single entity.
Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions
Gated recurrent neural networks have achieved remarkable results in the analysis of sequential data.
Improving Graph Convolutional Networks with Non-Parametric Activation Functions
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.
Complex-valued Neural Networks with Non-parametric Activation Functions
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
Neural networks are generally built by interleaving (adaptable) linear layers with (fixed) nonlinear activation functions.
Group Sparse Regularization for Deep Neural Networks
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).
Effective Blind Source Separation Based on the Adam Algorithm
In this paper, we derive a modified InfoMax algorithm for the solution of Blind Signal Separation (BSS) problems by using advanced stochastic methods.
Learning activation functions from data using cubic spline interpolation
Neural networks require a careful design in order to perform properly on a given task.
