Marco Ciccone

TL;DR: In this paper, the authors propose two neural networks architectures for object detection: YOLE which integrates the events into surfaces and uses a frame-based model to process them, and fcYOLE, an asynchronous event-based fully convolutional network which uses a novel and general formalization of the convolution and max pooling layers to exploit the sparsity of camera events.

TL;DR: A structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks to retrieve distant dependencies, based on the recently introduced ReNet model for image classification is proposed.

TL;DR: In this article, a cluster-driven Graph Federated Learning (FedCG) model is proposed to address statistical heterogeneity in federated learning, where graph convolutional networks (GCNs) enable sharing knowledge across domains.

TL;DR: In this paper, a grid of Long Short-Term Memory (LSTM) cells is proposed to learn end-to-end task-dependent event-surfaces, which shows good flexibility and expressiveness on optical flow estimation.