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Found 6 papers, 2 papers with code
Random Projections of Sparse Adjacency Matrices
We analyze a random projection method for adjacency matrices, studying its utility in representing sparse graphs.
Tensor Products and Hyperdimensional Computing
Following up on a previous analysis of graph embeddings, we generalize and expand some results to the general setting of vector symbolic architectures (VSA) and hyperdimensional computing (HDC).
Commutativity and Disentanglement from the Manifold Perspective
In this paper, we interpret disentanglement as the discovery of local charts of the data manifold and trace how this definition naturally leads to an equivalent condition for disentanglement: commutativity between factors of variation.
Memory and Capacity of Graph Embedding Methods
THIS PAPER IS NOW DEFUNCT: Check out "Graph Embeddings via Tensor Products and Approximately Orthonormal Codes", where it has been combined into one paper.
Graph Embeddings via Tensor Products and Approximately Orthonormal Codes
We propose a dynamic graph representation method, showcasing its rich representational capacity and establishing some of its theoretical properties.
Disentangling images with Lie group transformations and sparse coding
Discrete spatial patterns and their continuous transformations are two important regularities contained in natural signals.
