8 citations · 11 across the 5 of their papers we have counts for
6 papers
Graph Neural Networks for Microbial Genome Recovery
Andre Lamurias, Alessandro Tibo, Katja Hose +2
Microbes have a profound impact on our health and environment, but our understanding of the diversity and function of microbial communities is severely limited. Through DNA sequenc…
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…
Off the Beaten Track: Using Deep Learning to Interpolate Between Music Genres
Tijn Borghuis, Alessandro Tibo, Simone Conforti +3
We describe a system based on deep learning that generates drum patterns in the electronic dance music domain. Experimental results reveal that generated patterns can be employed t…