Introduces TSBM, a new Bayesian model for directed networks that enforces ordered blocks via transitivity-inducing priors on directional imbalance and jointly infers block count with an age-ordered partition prior.
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representative citing papers
Introduces the AR(1)-MSBM for evolving multilayer networks and provides online estimators with minimax-optimal rates and community recovery guarantees under stationarity and non-stationarity via adaptive windowing.
A large benchmark finds traditional imputation methods for scRNA-seq data generally outperform deep learning ones, but numerical recovery does not reliably improve biological downstream analyses and no method wins across all settings.
SteeringDRL identifies two optimization regimes in diffusion autoencoders and uses gated residual U-Nets with a log SNR curriculum to steer training toward disentangled representations, improving performance across multiple benchmarks.
HEP is a hierarchical point process model that superposes time-evolving excitation kernels to capture stimulus-driven event times and clusters latent response dynamics via likelihood inference.
SBTA reformulates topic modeling to assign topics at the segment level rather than document level, yielding cleaner topics on a new SemEval-STM dataset created via LLM decomposition and human refinement.
CADI quantifies the preservation of relative cluster angles in low-dimensional projections using internal angles from point triples.
Machine translation preserves embedding similarity structure for ten languages but distorts it for four in the Manifesto Corpus, via a new non-inferiority testing framework.
Four failure archetypes emerge from 400 failed network-segmentation projects, with campus and VLAN-style projects overrepresented in the most failure-intensive groups.
A modular systemisation plus practitioner objectives framework organises DP graph release methods and yields an open social-network benchmark of SotA edge- and node-DP algorithms.
Extends blurring mean shift to functional data in Hilbert space with convergence analysis and a scalable stochastic variant based on random partitioning.
Proposes an exploratory diagnostic workflow to highlight behavioral variation along MORL Pareto fronts not captured by objective values, with validation on grid and continuous control tasks.
A nonmonotone subgradient algorithm is developed for upper-C^2 optimization on submanifolds with stationarity and KL-based convergence guarantees.
LLM digital personas improve alignment with human survey response distributions for stable attributes but remain limited for individual prediction and fail to recover multivariate respondent structure.
ProfileGLMM is an R package extending Bayesian profile regression with GLMMs to support hierarchical data, random effects, and cluster-covariate interactions for continuous or binary outcomes.
A pathway-constrained autoencoder extended to multi-omics integration improves breast cancer stratification and provides interpretable pathway activity scores.
MtFAD plus MOBSynC on GAMA data yields eight simple clusters that merge into red and blue sequences containing substructure tied to mass quenching, environmental quenching, morphology and environment.
The effectiveness of dimensionality reduction before clustering depends on matching the specific technique and target dimension count to the data geometry and the clustering algorithm used.
Football fever in spectators follows a V-shaped time course captured as a latent process from heart rate and stress data via time-dependent structural equation modeling.
An unsupervised-to-supervised ML pipeline on UK NDNS data discovers four dietary patterns, reproduces them with macro-F1 0.963 using a surrogate classifier, and interprets them via SHAP for potential clinical use.
citing papers explorer
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A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data
A large benchmark finds traditional imputation methods for scRNA-seq data generally outperform deep learning ones, but numerical recovery does not reliably improve biological downstream analyses and no method wins across all settings.
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An Explainable Unsupervised-to-Supervised Machine Learning Framework for Dietary Pattern Discovery Using UK National Dietary Survey Data
An unsupervised-to-supervised ML pipeline on UK NDNS data discovers four dietary patterns, reproduces them with macro-F1 0.963 using a surrogate classifier, and interprets them via SHAP for potential clinical use.