Statistical Learning
Variational Gaussian Processes For Linear Inverse Problems
By now Bayesian methods are routinely used in practice for solving inverse problems.
On Class Distributions Induced by Nearest Neighbor Graphs for Node Classification of Tabular Data
In disciplines such as chemistry, physics, and network science, graphs are a convenient and well-studied mathematical object to represent such interacting entities and their attributes.
Supplementary Material for Learning Energy-based Model via Dual-MCMC Teaching
We show additional image synthesis in Fig.2. For reported numbers in main text, we adopt the network structure that contains Residue Blocks (see implementation details in Tab.5). We then test our model for the task of image inpainting. As shown in Fig.1, our This is the marginal version of Eqn.8 shown in the main text. 2 2.3 Learning Algorithm Three models are trained in an alternative and iterative manner based on the current model parameters. Compared to Eqn.3 and Eqn.6 in the main text, Eqn.5 and Eqn.6 start with initial points initialized We present the learning algorithm in Alg.1.
Semi-Supervised Domain Generalization with Known and Unknown Classes
Semi-Supervised Domain Generalization (SSDG) aims to learn a model that is generalizable to an unseen target domain with only a few labels, and most existing SSDG methods assume that unlabeled training and testing samples are all known classes. However, a more realistic scenario is that known classes may be mixed with some unknown classes in unlabeled training and testing data.
