activity
20202024
most citedSuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems

1 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI2024

Clustering Dynamics for Improved Speed Prediction Deriving from Topographical GPS Registrations

Sarah Almeida Carneiro, Giovanni Chierchia, Aurelie Pirayre +1

A persistent challenge in the field of Intelligent Transportation Systems is to extract accurate traffic insights from geographic regions with scarce or no data coverage. To this e…

cs.LG20231 cited

SWMLP: Shared Weight Multilayer Perceptron for Car Trajectory Speed Prediction using Road Topographical Features

Sarah Almeida Carneiro, Giovanni Chierchia, Jean Charléty +2

Although traffic is one of the massively collected data, it is often only available for specific regions. One concern is that, although there are studies that give good results for…

cs.LG20231 cited

Domain-Aware Augmentations for Unsupervised Online General Continual Learning

Nicolas Michel, Romain Negrel, Giovanni Chierchia +1

Continual Learning has been challenging, especially when dealing with unsupervised scenarios such as Unsupervised Online General Continual Learning (UOGCL), where the learning agen…

cs.LG20231 cited

New metrics for analyzing continual learners

Nicolas Michel, Giovanni Chierchia, Romain Negrel +2

Deep neural networks have shown remarkable performance when trained on independent and identically distributed data from a fixed set of classes. However, in real-world scenarios, i…

cs.LG20221 cited

Contrastive Learning for Online Semi-Supervised General Continual Learning

Nicolas Michel, Romain Negrel, Giovanni Chierchia +1

We study Online Continual Learning with missing labels and propose SemiCon, a new contrastive loss designed for partly labeled data. We demonstrate its efficiency by devising a mem…

q-fin.CP20201 cited

SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems

Pooja Gupta, Angshul Majumdar, Emilie Chouzenoux +1

This work proposes a supervised multi-channel time-series learning framework for financial stock trading. Although many deep learning models have recently been proposed in this dom…