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Histopathology whole slide image analysis with heterogeneous graph representation learning
Graph-based methods have been extensively applied to whole slide histopathology image
(WSI) analysis due to the advantage of modeling the spatial relationships among different …
(WSI) analysis due to the advantage of modeling the spatial relationships among different …
Position: Bayesian deep learning is needed in the age of large-scale AI
In the current landscape of deep learning research, there is a predominant emphasis on
achieving high predictive accuracy in supervised tasks involving large image and language …
achieving high predictive accuracy in supervised tasks involving large image and language …
Identifiable deep generative models via sparse decoding
We develop the sparse VAE for unsupervised representation learning on high-dimensional
data. The sparse VAE learns a set of latent factors (representations) which summarize the …
data. The sparse VAE learns a set of latent factors (representations) which summarize the …
New paradigm of identifiable general-response cognitive diagnostic models: beyond categorical data
Cognitive diagnostic models (CDMs) are a popular family of discrete latent variable models
that model students' mastery or deficiency of multiple fine-grained skills. CDMs have been …
that model students' mastery or deficiency of multiple fine-grained skills. CDMs have been …
Restricted latent class models for nominal response data: Identifiability and estimation
Restricted latent class models (RLCMs) provide an important framework for diagnosing and
classifying respondents on a collection of multivariate binary responses. Recent research …
classifying respondents on a collection of multivariate binary responses. Recent research …
Learning Discrete Concepts in Latent Hierarchical Models
Learning concepts from natural high-dimensional data (eg, images) holds potential in
building human-aligned and interpretable machine learning models. Despite its …
building human-aligned and interpretable machine learning models. Despite its …
Going deep in diagnostic modeling: Deep cognitive diagnostic models (DeepCDMs)
Cognitive diagnostic models (CDMs) are discrete latent variable models popular in
educational and psychological measurement. In this work, motivated by the advantages of …
educational and psychological measurement. In this work, motivated by the advantages of …
Without Pain--Clustering Categorical Data Using a Bayesian Mixture of Finite Mixtures of Latent Class Analysis Models
We propose a Bayesian approach for model-based clustering of multivariate categorical
data where variables are allowed to be associated within clusters and the number of clusters …
data where variables are allowed to be associated within clusters and the number of clusters …
A Review of Bayesian Methods for Infinite Factorisations
M Grushanina - arxiv preprint arxiv:2309.12990, 2023 - arxiv.org
Defining the number of latent factors has been one of the most challenging problems in
factor analysis. Infinite factor models offer a solution to this problem by applying increasing …
factor analysis. Infinite factor models offer a solution to this problem by applying increasing …
Differentiable Causal Discovery For Latent Hierarchical Causal Models
Discovering causal structures with latent variables from observational data is a fundamental
challenge in causal discovery. Existing methods often rely on constraint-based, iterative …
challenge in causal discovery. Existing methods often rely on constraint-based, iterative …