20 papers · ranked by Valyu relevance
Kyle Bojanek, Olivier Marre, Stephanie E. Palmer
Populations of sensory neurons are thought to be shaped by selective pressures for optimal information transmission, yet real neural circuits display substantial variability across stimulus repeats, across time, and between individuals. Reconciling this variability with normative theories requires understanding not…
Simon Matrenok, Ekaterina L. Andrianova, Konstantin Avchaciov, Daria I. Fleyshman + 7 more
Aging was tracked in a cohort of 99 “retired” sled dogs over four years to characterize the latent dynamics of physiological decline. Animals were randomized to receive either placebo or the reverse transcriptase inhibitor lamivudine for ∼30 months. We employed a variational autoregressive model to integrate…
Jing Ouyang, Chengyu Cui, Yunxiao Chen, Kean Ming Tan + 1 more
Regression models with both high-dimensional responses and covariates have attracted growing attention. Standard multivariate regression models become inadequate when the response variables depend not only on observed covariates but also on latent variables that capture key unobserved characteristics. To draw…
Huaming Du, Tao Hu, Yijie Huang, Yu Zhao + 4 more
Revealing the underlying causal mechanisms in the real world is crucial for scientific and technological progress. Despite notable advances in recent decades, the lack of high-quality data and the reliance of traditional causal discovery algorithms (TCDA) on the assumption of no latent confounders, as well as their…
Bambang Widjanarko Otok, Zulfani Alfasanah, Diaz Fitra Aksioma
Title: Highlights 1. • PCA is used in the inner weighting scheme of the PLS model to obtain latent variable scores. 2. • PLS-IPA explains the influence between latent variables and indicators while mapping indicators based on importance and performance.
Emily F. Wisinski, Maria J. Molina, Kyle J. C. Hall, Hannah Bao + 3 more
What is encoded in the latent space of a multi-branch $β$-variational autoencoder trained on coupled tropical Pacific climate fields? To answer this question, we train the model on sea surface temperature, ocean heat content, and outgoing longwave radiation across the tropical Pacific, using a 500-year preindustrial…
Fatih Dinc, Marta Blanco-Pozo, David Klindt, Francisco Acosta + 8 more
Many neural recordings have revealed low-dimensional sets of behaviorally relevant variables encoded within large-scale neural activity patterns. However, dimensionality reduction analyses alone cannot yield causal explanations for how networks stably implement computations that are resilient to the substantial…
Linxi Li, Rong Li, Shuangge Ma, Qingzhao Zhang
Graphical models serve as fundamental tools for encoding conditional dependence structures in multivariate biological data, with latent variable Gaussian graphical models playing a pivotal role in capturing complex dependencies in the presence of unobserved confounding variables. However, practical implementations…
Julien P. Irmer, Karin Schermelleh-Engel, Peter Schmidt
The contributions in this special issue collectively reflect the breadth of current methodological developments for modeling conditional and context-dependent effects with latent variables and highlight the practical challenges that arise when studying complex mediated and moderated processes with latent variables.…
Authors not listed
Machine learning models are increasingly applied to heterogeneous materials datasets spanning different synthesis routes, measurement protocols, and structural classes. Although multi-task and representation-learning approaches are commonly used to improve predictive performance, the latent representations learned by…
Takashi Arai
We develop a factor analysis for mixed continuous and binary observed variables. To this end, we utilized a recently developed multivariate probability distribution for mixed-type random variables, the Gaussian-Grassmann distribution. In the proposed factor analysis, marginalization over latent variables can be…
George Hutchings, Pantelis Samartsidis, Corinne Donnay, Laura Gaetano + 5 more
Probabilistic latent variable models are a powerful tool for uncovering structure in high-dimensional datasets, particularly in biomedical applications. The increasing availability of large-scale epidemiological studies, such as the UK Biobank, poses important modelling challenges, including mixed data types, high…
Yifan Lin, Kevin Z. Lin
High-dimensional genomics studies are frequently confounded by unmeasured biological processes that obscure disease-specific signals. While existing workflows can estimate these latent confounders, they fail to quantify how robust a discovery is to varying levels of hypothetical confounding. We introduce sensGAN, a…
Anja F. Ernst, Jonas M. B. Haslbeck
Time-series data have become ubiquitous in psychological research, allowing us to study detailed within-person dynamics and their heterogeneity across persons. Vector autoregressive (VAR) models have become a popular choice as a first approximation of these dynamics. The VAR model for each person and heterogeneity…
Kyungmin Lim, Su-Young Kim
In the structural equation modeling framework, binary variable models are generally considered a special case of ordinal variable models, as both involve similar scale assignment processes. However, the scaling processes of the two model types differ, with these differences becoming increasingly pronounced in the…
Authors not listed
The discovery of chemically novel or structurally anomalous metal-organic frameworks (MOFs) is essential for expanding reticular design space and enhancing dataset reliability. We present CHEM-AD (Chemically Unusual Metal–organic Frameworks via Autoencoder-based Detection), a label-free, CPU-efficient pipeline that…
Tamara Schamberger, Florian Schuberth, Jörg Henseler
Moderated mediation models are crucial in many disciplines, particularly the social sciences. Researchers use them to analyze the conditions under which different variables are related. Structural equation modeling (SEM) is an eminently suitable framework for this endeavor. In fact, several approaches have been…
Authors not listed
Collective variables (CVs) are essential for interpreting and accelerating rare events in molecular simulations. However, their design remains limited by the requirement of differentiability with respect to atomic coordinates. This constraint excludes many powerful structural descriptors that are routinely used for…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Nadav Kunievsky
The interpretation of coefficients from multivariate linear regression relies on the assumption that the conditional expectation function is linear in the variables. However, in many cases the underlying data generating process is nonlinear. This paper examines how to interpret regression coefficients under…