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Found 7 papers, 1 papers with code
Graph Neural Networks for Microbial Genome Recovery
In this paper, we propose to use Graph Neural Networks (GNNs) to leverage the assembly graph when learning contig representations for metagenomic binning.
Inducing Gaussian Process Networks
Gaussian processes (GPs) are powerful but computationally expensive machine learning models, requiring an estimate of the kernel covariance matrix for every prediction.
Learning Aggregation Functions
Learning on sets is increasingly gaining attention in the machine learning community, due to its widespread applicability.
A general framework for defining and optimizing robustness
Robustness of neural networks has recently attracted a great amount of interest.
Learning and Interpreting Multi-Multi-Instance Learning Networks
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 sentences, which in turn are bags of words).
Extended pipeline for content-based feature engineering in music genre recognition
We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical genre identification.
Off the Beaten Track: Using Deep Learning to Interpolate Between Music Genres
We describe a system based on deep learning that generates drum patterns in the electronic dance music domain.
