8 citations · 11 across the 5 of their papers we have counts for
4 papers · 1 filter
Inducing Gaussian Process Networks
Alessandro Tibo, Thomas Dyhre Nielsen
Gaussian processes (GPs) are powerful but computationally expensive machine learning models, requiring an estimate of the kernel covariance matrix for every prediction. In large an…
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…
A general framework for defining and optimizing robustness
Alessandro Tibo, Manfred Jaeger, Kim G. Larsen
Robustness of neural networks has recently attracted a great amount of interest. The many investigations in this area lack a precise common foundation of robustness concepts. There…
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…