Search · four archives
Search · four archives
13 papers · ranked by Valyu relevance
Mahbod Nouri, David Rotermund, Alberto Garcia-Ortiz, Klaus R. Pawelzik
Considering biological constraints in artificial neural networks has led to dramatic improvements in performance. Nevertheless, to date, the positivity of long-range signals in the cortex has not been shown to yield improvements. While Non-negative matrix factorization (NMF) captures biological constraints of positive…
Ragnhild Laursen, Han Chen, Jack Demaray, Karin Pelka + 1 more
Methods for identifying complex multicellular spatial neighborhoods do not scale to existing spatial transcriptomics data, and often divide tissues into distinct neighborhoods with hard borders. We develop neighborhood NMF (NNMF) that identifies functionally coherent neighborhoods among heterogeneous cells. NNMF scales…
Cristian Castiglione, Alexandre Segers, Lieven Clement, Davide Risso
Title: Summary Single-cell RNA sequencing allows the quantification of gene expression at the individual cell level, enabling the study of cellular heterogeneity and gene expression dynamics. Dimensionality reduction is a common preprocessing step critical for the visualization, clustering, and phenotypic…
Jin Deng, Junjie Lan, Ruolan Du, Tao Xu + 4 more
The high recurrence rate of tumor limits the growth of precision medicine, whereas the exploration of correlations in multimodal data enables mining of features linked to tumor recurrence, ultimately identifying prospective biomarkers. Nevertheless, existing multimodal approaches centered on genetic molecular data…
Lara Kassab, Erin George, Deanna Needell, Haowen Geng + 2 more
There has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed alongside bias mitigation strategies that are practical and approachable to the practitioner. Motivated by recent work on…
Jing Wu, Wenbo Li, Lijun Su, Huiru Wang + 2 more
In this paper, we present a modified nonmonotone line search algorithm that employs a variable parameter to control the degree of nonmonotonicity. This modification enhances both the probability of identifying the global minimum and the rate of convergence. Within the framework of alternating nonnegative least squares…
SeungJoo Lee, Yong-Chan Park, U. Kang, George Vousden
How can we accurately decompose a temporal irregular tensor along while incorporating a related knowledge graph tensor in both offline and online streaming settings? PARAFAC2 decomposition is widely applied to the analysis of irregular tensors consisting of matrices with varying row sizes. In both offline and online…
Peter Carbonetto, Abhishek Sarkar, Zihao Wang, Matthew Stephens
In an effort to develop topic modeling methods that can be quickly applied to large data sets, we revisit the problem of maximum-likelihood estimation in topic models. It is known, at least informally, that maximum-likelihood estimation in topic models is closely related to non-negative matrix factorization (NMF). Yet…
Shuta Kikuchi, Shu Tanaka
The RNA inverse folding problem aims to identify nucleotide sequences that preferentially adopt a given target secondary structure. While various heuristic and machine learning-based approaches have been proposed, many require a large number of sequence evaluations, which limits their applicability when experimental…
András Telcs, Raúl Alcaraz
We develop a finite-resolution empirical framework for applying nonnegative Mages-Anastasiadi-Rohner partial information decomposition (MAR-PID) to continuous and non-binary discrete variables. The variables are represented by recursive quantile binarization. This provides a balanced binary-tree representation at each…
Calvin Yeung, Prathyush Poduval, Mohsen Imani
Hopfield networks retrieve stored patterns through recurrent nonlinear updates, and later associative-memory variants broaden this design space through alternative nonlinear retrieval rules. Resonator networks use an analogous iterative procedure to solve vector-symbolic factorization: each factor estimate is updated…
Daniela Calvetti, Erkki Somersalo
A central goal in many brain studies is the identification of those brain regions that are activated during an observation window that may correspond to a motor task, a stimulus, or simply a resting state. While functional MRI is currently the most commonly employed modality for such task, methods based on the…
Tao Guo, Zhangyao Song, Huihui Wu, Yang Li + 1 more
This paper analyzes the semantic rate-distortion problem motivated by task-oriented data compression with side information. The semantic information related to a task is not directly accessible to the encoder but implicitly impacts the observations through a joint probability distribution. The decoder aims to…