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Found 8 papers, 4 papers with code
Extending Transductive Knowledge Graph Embedding Models for Inductive Logical Relational Inference
In this work, we bridge the gap between traditional transductive knowledge graph embedding approaches and more recent inductive relation prediction models by introducing a generalized form of harmonic extension which leverages representations learned through transductive embedding methods to infer representations of new entities introduced at inference time as in the inductive setting.
Graph Convolutional Networks from the Perspective of Sheaves and the Neural Tangent Kernel
Graph convolutional networks are a popular class of deep neural network algorithms which have shown success in a number of relational learning tasks.
Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding
Knowledge graph embedding involves learning representations of entities -- the vertices of the graph -- and relations -- the edges of the graph -- such that the resulting representations encode the known factual information represented by the knowledge graph and can be used in the inference of new relations.
A Unified Paths Perspective for Pruning at Initialization
A number of recent approaches have been proposed for pruning neural network parameters at initialization with the goal of reducing the size and computational burden of models while minimally affecting their training dynamics and generalization performance.
Sheaf Neural Networks
We present a generalization of graph convolutional networks by generalizing the diffusion operation underlying this class of graph neural networks.
Path homologies of deep feedforward networks
These results provide a foundation for investigating homological differences between neural network architectures and their realized structure as implied by their parameters.
Characterizing the Shape of Activation Space in Deep Neural Networks
The representations learned by deep neural networks are difficult to interpret in part due to their large parameter space and the complexities introduced by their multi-layer structure.
Adversary Detection in Neural Networks via Persistent Homology
We outline a detection method for adversarial inputs to deep neural networks.
