research
∙
05/02/2023
Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation
Trustworthy Artificial Intelligence (AI) is based on seven technical req...
share
research
∙
12/06/2022
Towards a more efficient computation of individual attribute and policy contribution for post-hoc explanation of cooperative multi-agent systems using Myerson values
A quantitative assessment of the global importance of an agent in a team...
research
∙
09/26/2022
Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification
Although Deep Neural Networks (DNNs) have great generalization and predi...
research
∙
07/18/2022
Capabilities, Limitations and Challenges of Style Transfer with CycleGANs: A Study on Automatic Ring Design Generation
Rendering programs have changed the design process completely as they pe...
research
∙
05/20/2022
Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization
There is a broad consensus on the importance of deep learning models in ...
research
∙
02/21/2022
OG-SGG: Ontology-Guided Scene Graph Generation. A Case Study in Transfer Learning for Telepresence Robotics
Scene graph generation from images is a task of great interest to applic...
research
∙
10/04/2021
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley Values
While Explainable Artificial Intelligence (XAI) is increasingly expandin...
research
∙
09/17/2021
Efficient State Representation Learning for Dynamic Robotic Scenarios
While the rapid progress of deep learning fuels end-to-end reinforcement...
research
∙
04/29/2021
Questioning causality on sex, gender and COVID-19, and identifying bias in large-scale data-driven analyses: the Bias Priority Recommendations and Bias Catalog for Pandemics
The COVID-19 pandemic has spurred a large amount of observational studie...
research
∙
04/24/2021
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case
The latest Deep Learning (DL) models for detection and classification ha...
research
∙
04/10/2021
Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization
As climate change increases the intensity of natural disasters, society ...
research
∙
04/02/2021
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
Most of state of the art methods applied on time series consist of deep ...
research
∙
03/15/2021
Explaining Credit Risk Scoring through Feature Contribution Alignment with Expert Risk Analysts
Credit assessments activities are essential for financial institutions a...
research
∙
10/16/2020
Physics-informed GANs for Coastal Flood Visualization
As climate change increases the intensity of natural disasters, society ...
research
∙
08/15/2020
Explainability in Deep Reinforcement Learning
A large set of the explainable Artificial Intelligence (XAI) literature ...
research
∙
05/25/2020
Should artificial agents ask for help in human-robot collaborative problem-solving?
Transferring as fast as possible the functioning of our brain to artific...
research
∙
05/13/2020
DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics
Robots are still limited to controlled conditions, that the robot design...
research
∙
03/26/2020
Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning models
Image captioning models have been able to generate grammatically correct...
research
∙
10/22/2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
In the last years, Artificial Intelligence (AI) has achieved a notable m...
research
∙
09/19/2019
Highlighting Bias with Explainable Neural-Symbolic Visual Reasoning
Many high-performance models suffer from a lack of interpretability. The...
research
∙
07/11/2019
DisCoRL: Continual Reinforcement Learning via Policy Distillation
In multi-task reinforcement learning there are two main challenges: at t...
research
∙
06/29/2019
Continual Learning for Robotics
Continual learning (CL) is a particular machine learning paradigm where ...
research
∙
06/11/2019
Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer
We focus on the problem of teaching a robot to solve tasks presented seq...
research
∙
01/24/2019
Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
Scaling end-to-end reinforcement learning to control real robots from vi...
research
∙
11/13/2018
Intelligent Drone Swarm for Search and Rescue Operations at Sea
In recent years, a rising numbers of people arrived in the European Unio...
research
∙
10/31/2018
Don't forget, there is more than forgetting: new metrics for Continual Learning
Continual learning consists of algorithms that learn from a stream of da...
research
∙
09/25/2018
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning
State representation learning aims at learning compact representations f...
research
∙
02/12/2018
State Representation Learning for Control: An Overview
Representation learning algorithms are designed to learn abstract featur...
research
∙
09/15/2017
Unsupervised state representation learning with robotic priors: a robustness benchmark
Our understanding of the world depends highly on our capacity to produce...
research
∙
03/30/2016
