105 citations · 199 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
Classification of cancer pathology reports: a large-scale comparative study
Stefano Martina, Leonardo Ventura, Paolo Frasconi
We report about the application of state-of-the-art deep learning techniques to the automatic and interpretable assignment of ICD-O3 topography and morphology codes to free-text ca…
MARTHE: Scheduling the Learning Rate Via Online Hypergradients
Michele Donini, Luca Franceschi, Massimiliano Pontil +2
We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization, aiming at good generalization. We describe the structure…
Learning and Interpreting Multi-Multi-Instance Learning Networks
Alessandro Tibo, Manfred Jaeger, Paolo Frasconi
We introduce an extension of the multi-instance learning problem where examples are organized as nested bags of instances (e.g., a document could be represented as a bag of sentenc…
Shift Aggregate Extract Networks
Francesco Orsini, Daniele Baracchi, Paolo Frasconi
We introduce an architecture based on deep hierarchical decompositions to learn effective representations of large graphs. Our framework extends classic R-decompositions used in ke…