9 citations · 21 across the 4 of their papers we have counts for
6 papers · 1 filter
Neuro-Symbolic Constraint Programming for Structured Prediction
Paolo Dragone, Stefano Teso, Andrea Passerini
We propose Nester, a method for injecting neural networks into constrained structured predictors. The job of the neural network(s) is to compute an initial, raw prediction that is…
Human-in-the-loop Handling of Knowledge Drift
Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini +1
We introduce and study knowledge drift (KD), a complex form of drift that occurs in hierarchical classification. Under KD the vocabulary of concepts, their individual distributions…
Learning Aggregation Functions
Giovanni Pellegrini, Alessandro Tibo, Paolo Frasconi +2
Learning on sets is increasingly gaining attention in the machine learning community, due to its widespread applicability. Typically, representations over sets are computed by usin…
Learning in the Wild with Incremental Skeptical Gaussian Processes
Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia +1
The ability to learn from human supervision is fundamental for personal assistants and other interactive applications of AI. Two central challenges for deploying interactive learne…
Few-Shot Unsupervised Continual Learning through Meta-Examples
Alessia Bertugli, Stefano Vincenzi, Simone Calderara +1
In real-world applications, data do not reflect the ones commonly used for neural networks training, since they are usually few, unlabeled and can be available as a stream. Hence m…
Efficient Generation of Structured Objects with Constrained Adversarial Networks
Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi +3
Generative Adversarial Networks (GANs) struggle to generate structured objects like molecules and game maps. The issue is that structured objects must satisfy hard requirements (e.…