no code implementations • EMNLP 2021 • Mojtaba Nayyeri, Chengjin Xu, Franca Hoffmann, Mirza Mohtashim Alam, Jens Lehmann, Sahar Vahdati
Many KGEs use the Euclidean geometry which renders them incapable of preserving complex structures and consequently causes wrong inferences by the models.
no code implementations • 2 Mar 2023 • Alfredo Garbuno-Inigo, Tapio Helin, Franca Hoffmann, Bamdad Hosseini
In recent years, Bayesian inference in large-scale inverse problems found in science, engineering and machine learning has gained significant attention.
no code implementations • 13 Sep 2019 • Franca Hoffmann, Bamdad Hosseini, Assad A. Oberai, Andrew M. Stuart
Graph Laplacians computed from weighted adjacency matrices are widely used to identify geometric structure in data, and clusters in particular; their spectral properties play a central role in a number of unsupervised and semi-supervised learning algorithms.
no code implementations • 18 Jun 2019 • Franca Hoffmann, Bamdad Hosseini, Zhi Ren, Andrew M. Stuart
Graph-based semi-supervised learning is the problem of propagating labels from a small number of labelled data points to a larger set of unlabelled data.
no code implementations • 30 Jan 2019 • Nicolas Garcia Trillos, Franca Hoffmann, Bamdad Hosseini
More precisely, we assume that the data is sampled from a mixture model supported on a manifold $\mathcal{M}$ embedded in $\mathbb{R}^d$, and pick a connectivity length-scale $\varepsilon>0$ to construct a kernelized graph Laplacian.