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Found 4 papers, 1 papers with code
Representing Hyperbolic Space Accurately using Multi-Component Floats
Hyperbolic space is particularly useful for embedding data with hierarchical structure; however, representing hyperbolic space with ordinary floating-point numbers greatly affects the performance due to its \emph{ineluctable} numerical errors.
Random Reshuffling is Not Always Better
Many learning algorithms, such as stochastic gradient descent, are affected by the order in which training examples are used.
Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models
Hyperbolic embeddings achieve excellent performance when embedding hierarchical data structures like synonym or type hierarchies, but they can be limited by numerical error when ordinary floating-point numbers are used to represent points in hyperbolic space.
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms
Stochastic gradient descent (SGD) is a ubiquitous algorithm for a variety of machine learning problems.
