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Found 9 papers, 6 papers with code
Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery
By one of the most fundamental principles in physics, a dynamical system will exhibit those motions which extremise an action functional.
Distributed representations of graphs for drug pair scoring
In this paper we study the practicality and usefulness of incorporating distributed representations of graphs into models within the context of drug pair scoring.
PyRelationAL: a python library for active learning research and development
Active learning (AL) is a sub-field of ML focused on the development of methods to iteratively and economically acquire data through strategically querying new data points that are the most useful for a particular task.
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing.
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing.
Using ontology embeddings for structural inductive bias in gene expression data analysis
Stratifying cancer patients based on their gene expression levels allows improving diagnosis, survival analysis and treatment planning.
Incorporating network based protein complex discovery into automated model construction
We propose a method for gene expression based analysis of cancer phenotypes incorporating network biology knowledge through unsupervised construction of computational graphs.
Learning distributed representations of graphs with Geo2DR
We present Geo2DR (Geometric to Distributed Representations), a GPU ready Python library for unsupervised learning on graph-structured data using discrete substructure patterns and neural language models.
Decoupling feature propagation from the design of graph auto-encoders
We present two instances, L-GAE and L-VGAE, of the variational graph auto-encoding family (VGAE) based on separating feature propagation operations from graph convolution layers typically found in graph learning methods to a single linear matrix computation made prior to input in standard auto-encoder architectures.
