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Found 9 papers, 2 papers with code
Meta-Path Learning for Multi-relational Graph Neural Networks
Existing multi-relational graph neural networks use one of two strategies for identifying informative relations: either they reduce this problem to low-level weight learning, or they rely on handcrafted chains of relational dependencies, called meta-paths.
Learning Aggregation Functions
Learning on sets is increasingly gaining attention in the machine learning community, due to its widespread applicability.
Approximating Euclidean by Imprecise Markov Decision Processes
Second, we use imprecise Markov decision process approximations as a tool to analyse and validate cost functions and strategies obtained by reinforcement learning.
A general framework for defining and optimizing robustness
Robustness of neural networks has recently attracted a great amount of interest.
A Complete Characterization of Projectivity for Statistical Relational Models
As a by-product we also obtain a characterization for when a given distribution over size-$k$ structures is the statistical frequency distribution of size-$k$ sub-structures in much larger size-$n$ structures.
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).
Inference, Learning, and Population Size: Projectivity for SRL Models
A subtle difference between propositional and relational data is that in many relational models, marginal probabilities depend on the population or domain size.
Numeric Input Relations for Relational Learning with Applications to Community Structure Analysis
We show how numerical input relations can very easily be used in the Relational Bayesian Network framework, and that existing inference and learning methods need only minor adjustments to be applied in this generalized setting.
Lower Complexity Bounds for Lifted Inference
Further strengthening earlier results, this is also shown to hold for approximate inference, and for knowledge bases not containing the equality predicate.
