An overview of key trustworthiness attributes and KPIs for trusted ML-based systems engineering
When deployed, machine-learning (ML) adoption depends on its ability to actually deliver the expected service safely, and to meet user expectations in terms of quality and continuity of service.
Non-Markov Decision Processes and Reinforcement Learning
We present non-Markov decision processes, where rewards and dynamics can depend on the history of events. This is contrast with Markov Decision Processes, where the dependency is limited to the last state and action.
Cooperating with machines
Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go).
Should we fear the robot revolution? (The correct answer is yes)
Advances in artificial intelligence and robotics may be leading to a new industrial revolution. This paper presents a model with the minimum necessary features to analyze the implications for inequality and output.
gnntf: A Flexible Deep Graph Neural Network Framework
This repository provides a framework for easy experimentation with Graph Neural Network (GNN) architectures by separating them from predictive components.
Augmentation-free unsupervised approach for point clouds
Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.