research
∙
07/06/2021
Intrinsic uncertainties and where to find them
We introduce a framework for uncertainty estimation that both describes ...
share
research
∙
06/25/2021
Data efficiency in graph networks through equivariance
We introduce a novel architecture for graph networks which is equivarian...
research
∙
05/28/2021
Symmetry-driven graph neural networks
Exploiting symmetries and invariance in data is a powerful, yet not full...
research
∙
03/25/2021
Beyond permutation equivariance in graph networks
We introduce a novel architecture for graph networks which is equivarian...
research
∙
09/03/2020
Distributed Online Optimization via Gradient Tracking with Adaptive Momentum
This paper deals with a network of computing agents aiming to solve an o...
research
∙
12/05/2019
Collective Learning
In this paper, we introduce the concept of collective learning (CL) whic...
research
∙
11/13/2019
Asynchronous Distributed Learning from Constraints
In this paper, the extension of the framework of Learning from Constrain...
research
∙
11/06/2019
DISROPT: a Python Framework for Distributed Optimization
In this paper we introduce DISROPT, a Python package for distributed opt...
research
∙
09/23/2019
Upper Body Pose Estimation Using Wearable Inertial Sensors and Multiplicative Kalman Filter
Estimating the limbs pose in a wearable way may benefit multiple areas s...
research
∙
05/31/2019
