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09/06/2023
Generalised Mutual Information: a Framework for Discriminative Clustering
In the last decade, recent successes in deep clustering majorly involved...
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04/27/2023
Interpretable Neural-Symbolic Concept Reasoning
Deep learning methods are highly accurate, yet their opaque decision pro...
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03/15/2023
Understanding Post-hoc Explainers: The Case of Anchors
In many scenarios, the interpretability of machine learning models is a ...
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02/07/2023
Sparse GEMINI for Joint Discriminative Clustering and Feature Selection
Feature selection in clustering is a hard task which involves simultaneo...
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10/12/2022
Generalised Mutual Information for Discriminative Clustering
In the last decade, recent successes in deep clustering majorly involved...
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09/19/2022
Concept Embedding Models
Deploying AI-powered systems requires trustworthy models supporting effe...
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05/27/2022
A Sea of Words: An In-Depth Analysis of Anchors for Text Data
Anchors [Ribeiro et al. (2018)] is a post-hoc, rule-based interpretabili...
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05/02/2022
A Multi-stage deep architecture for summary generation of soccer videos
Video content is present in an ever-increasing number of fields, both sc...
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11/16/2021
SMACE: A New Method for the Interpretability of Composite Decision Systems
Interpretability is a pressing issue for decision systems. Many post hoc...
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10/15/2021
Knowledge-driven Active Learning
In the last few years, Deep Learning models have become increasingly pop...
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06/07/2021
Active Speaker Detection as a Multi-Objective Optimization with Uncertainty-based Multimodal Fusion
It is now well established from a variety of studies that there is a sig...
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04/07/2020
From text saliency to linguistic objects: learning linguistic interpretable markers with a multi-channels convolutional architecture
A lot of effort is currently made to provide methods to analyze and unde...
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11/26/2019
Revisiting Deep Architectures for Head Motion Prediction in 360° Videos
Head motion prediction is an important problem with 360 videos, in parti...
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04/01/2019
Adaptive Bayesian Linear Regression for Automated Machine Learning
To solve a machine learning problem, one typically needs to perform data...
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02/27/2018
Adversarial Active Learning for Deep Networks: a Margin Based Approach
We propose a new active learning strategy designed for deep neural netwo...
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11/21/2014