6 accepted papers
Transformer-based large language models (LLMs) rely on contextual embeddings which generate different (continuous) representations for the same token depending on its surrounding context. Nonetheless, words and tokens typically have a limited number of senses (or meanings). We propose multi-sense em…
Randomized trace estimation is a popular technique to approximate the trace of an implicitly-defined matrix $A$ by averaging the quadratic form $x’Ax$ across several samples of a random vector $x$. This paper focuses on the application of randomized trace estimators on asynchronous computing environ…
Topological data analysis (TDA) is a powerful technique for extracting complex and valuable shape-related summaries of high-dimensional data. However, the computational demands of classical algorithms for computing TDA are exorbitant, and quickly become impractical for high-order characteristics. Qu…
Eigenvector embeddings have been widely used to study graph properties in signal processing, mining, and learning tasks. However, if a graph is changing dynamically, these embeddings have to be recomputed. In this work we introduce a novel matrix resolvent expansion-based projection scheme to update…
We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels. The proposed model learns pairs of interpretable latent representations for the nodes of directed graphs, and uses parameterized graph convolution…
This submission considers the problem of updating the rank-$k$ truncated Singular Value Decomposition (SVD) of matrices subject to the addition of new rows and/or columns over time. Such matrix problems represent an important computational kernel in applications such as Latent Semantic Indexing and…