Search Results for author:
Found 18 papers, 7 papers with code
Default Resilience and Worst-Case Effects in Financial Networks
In this paper we analyze the resilience of a network of banks to joint price fluctuations of the external assets in which they have shared exposures, and evaluate the worst-case effects of the possible default contagion.
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow
Multi-image super-resolution (MISR) allows to increase the spatial resolution of a low-resolution (LR) acquisition by combining multiple images carrying complementary information in the form of sub-pixel offsets in the scene sampling, and can be significantly more effective than its single-image counterpart.
Semi-supervised learning for joint SAR and multispectral land cover classification
Semi-supervised learning techniques are gaining popularity due to their capability of building models that are effective, even when scarce amounts of labeled data are available.
COVID-19 case data for Italy stratified by age class
The dataset described in this paper contains daily data about COVID-19 cases that occurred in Italy over the period from Jan. 28, 2020 to March 20, 2021, divided into ten age classes of the population, the first class being 0-9 years, the tenth class being 90 years and over.
Denoise and Contrast for Category Agnostic Shape Completion
The combined embedding inherits category-agnostic properties from the chosen pretext tasks.
RAN-GNNs: breaking the capacity limits of graph neural networks
In this paper, we investigate the recently proposed randomly wired architectures in the context of graph neural networks.
Ranked #20 on
Graph Property Prediction
on ogbg-molpcba
Don't stack layers in graph neural networks, wire them randomly
In this paper, we investigate the recently proposed randomly wired architectures in the context of graph neural networks.
Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives
Synthetic aperture radar (SAR) images are affected by a spatially-correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation.
Learning Graph-Convolutional Representations for Point Cloud Denoising
Point clouds are an increasingly relevant data type but they are often corrupted by noise.
Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
Information extraction from synthetic aperture radar (SAR) images is heavily impaired by speckle noise, hence despeckling is a crucial preliminary step in scene analysis algorithms.
Towards Deep Unsupervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
The proposed method is trained employing only noisy images and can therefore learn features of real SAR images rather than synthetic data.
DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images
Deep learning methods for super-resolution of a remote sensing scene from multiple unregistered low-resolution images have recently gained attention thanks to a challenge proposed by the European Space Agency.
Survival and Neural Models for Private Equity Exit Prediction
Within the Private Equity (PE) market, the event of a private company undertaking an Initial Public Offering (IPO) is usually a very high-return one for the investors in the company.
Sparse $\ell_1$ and $\ell_2$ Center Classifiers
We show that training of the proposed sparse models, with both distance criteria, can be performed exactly (i. e., the globally optimal set of features is selected) and at a quasi-linear computational cost.
Deep Graph-Convolutional Image Denoising
The graph convolution operation generalizes the classic convolution to arbitrary graphs.
Ranked #3 on
Grayscale Image Denoising
on Set12 sigma25
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images
This novel framework integrates the spatial registration task directly inside the CNN, and allows to exploit the representation learning capabilities of the network to enhance registration accuracy.
Ranked #8 on
Multi-Frame Super-Resolution
on PROBA-V
Image Denoising with Graph-Convolutional Neural Networks
The graph-convolutional layers dynamically construct neighborhoods in the feature space to detect latent correlations in the feature maps produced by the hidden layers.
Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
We also study the problem of defining an upsampling layer in the graph-convolutional generator, such that it learns to exploit a self-similarity prior on the data distribution to sample more effectively.
