A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Bayesian approach to data association using Gaussian processes.
A novel cross-modal auto-encoder associates different data types efficiently.
Develops G-MLKM for better data-target association in constrained spaces.
DeepDA uses LSTM to track multiple targets in clutter.
New method discovers nonlinear associations using copula entropy.
GFA model uncovers brain-behavior associations in incomplete data sets.
Framework uncovers symmetric and asymmetric species associations from data.
New KNN test improves association analysis of high-dimensional sequencing data.
The paper tests for association between latent community memberships in multi-view network data.
Proposes JACA for joint analysis of multi-view data with class information.
A simple framework Probabilistic Multi-view Graph Embedding (PMvGE) is proposed for multi-view feature learning with many-to-many associations so that it generalizes various existing multi-view methods. PMvGE is a probabilistic model for predicting new associations via graph embedding of the nodes of data vectors with …
Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for association rules. However, these measures fail to take the probabilistic properties of the mined data into account. In this paper, we start …
The paper develops methods to identify stable associations across multiple studies.
Unified Bayesian framework for clustered federated learning improves model performance.
This paper gives two methods for constructing associative 3-folds in R^7, based around the fundamental idea of evolution equations, and uses these methods to construct examples of these geometric objects. The paper is a generalisation of the work by Joyce in math.DG/0008021, math.DG/0008155, math.DG/0010036 and math.DG…
DAM with MRL improves relational reasoning in MANNs.
For most problems in science and engineering we can obtain data sets that describe the observed system from various perspectives and record the behavior of its individual components. Heterogeneous data sets can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly ass…
New method for estimating spatial associations with discrete data, even under model misspecification.
Discovering and clustering subspaces in high-dimensional data is a fundamental problem of machine learning with a wide range of applications in data mining, computer vision, and pattern recognition. Earlier methods divided the problem into two separate stages of finding the similarity matrix and finding clusters. Simil…
Graph network predicts circRNA-disease associations using multi-source similarity features.
Given genetic variations and various phenotypical traits, such as Magnetic Resonance Imaging (MRI) features, we consider two important and related tasks in biomedical research: i)to select genetic and phenotypical markers for disease diagnosis and ii) to identify associations between genetic and phenotypical data. Thes…
We present a procedure for effective estimation of entropy and mutual information from small-sample data, and apply it to the problem of inferring high-dimensional gene association networks. Specifically, we develop a James-Stein-type shrinkage estimator, resulting in a procedure that is highly efficient statistically …
The method integrates survival constraints into NMF for identifying survival-associated gene clusters.
Genome-wide association studies have proven to be essential for understanding the genetic basis of disease. However, many complex traits---personality traits, facial features, disease subtyping---are inherently high-dimensional, impeding simple approaches to association mapping. We developed a nonparametric Bayesian re…
HNet detects significant associations in mixed data types efficiently.
DEDACT breaks down feature importance into direct and associative components.
We prove the existence of "half-plane differentials" with prescribed local data on any Riemann surface. These are meromorphic quadratic differentials with higher-order poles which have an associated singular flat metric isometric to a collection of euclidean half-planes glued by an interval-exchange map on their bounda…
Cluster analysis methods are used to identify homogeneous subgroups in a data set. In biomedical applications, one frequently applies cluster analysis in order to identify biologically interesting subgroups. In particular, one may wish to identify subgroups that are associated with a particular outcome of interest. Con…
This paper is based on a previous publication [29]. Our work extends exception mining and outlier detection to the case of object-relational data. Object-relational data represent a complex heterogeneous network [12], which comprises objects of different types, links among these objects, also of different types, and at…
Memory capacity of DAM scales exponentially with feature separation, unaffected by correlations.
Second generation sequencing technologies are being increasingly used for genetic association studies, where the main research interest is to identify sets of genetic variants that contribute to various phenotype. The phenotype can be univariate disease status, multivariate responses and even high-dimensional outcomes.…
The article develops deformation theory for ACyl associative submanifolds in ACyl G2-manifolds.
A federated model learns shared archetypes from heterogeneous clients in continual learning.
DMs emerge from DenseAMs, transitioning from memorization to generalization.
Develops methods to analyze feature-outcome associations in subpopulations.
Model learns association between text and speech without paired data.
Data-target association is an important step in multi-target localization for the intelligent operation of un- manned systems in numerous applications such as search and rescue, traffic management and surveillance. The objective of this paper is to present an innovative data association learning approach named multi-la…
In this work we introduce a mixture of GPs to address the data association problem, i.e. to label a group of observations according to the sources that generated them. Unlike several previously proposed GP mixtures, the novel mixture has the distinct characteristic of using no gating function to determine the associati…
Proposes AAA for efficient association estimation with confounders.
The paper explores new rules for analyzing label rankings and pairwise preferences.
Deep learning method clusters multi-view data matrices.
Auto-Associative models cover a large class of methods used in data analysis. In this paper, we describe the generals properties of these models when the projection component is linear and we propose and test an easy to implement Probabilistic Semi-Linear Auto- Associative model in a Gaussian setting. We show it is a g…
This paper deals with the binary classification task when the target class has the lower probability of occurrence. In such situation, it is not possible to build a powerful classifier by using standard methods such as logistic regression, classification tree, discriminant analysis, etc. To overcome this short-coming o…
CrossAD detects anomalies in time series data by considering cross-scale associations and cross-window modeling.
New product structures encode superintegrable Hamiltonian systems in Euclidean spaces.
A test for neural networks identifies genetic associations.
Despite tremendous progress in outlier detection research in recent years, the majority of existing methods are designed only to detect unconditional outliers that correspond to unusual data patterns expressed in the joint space of all data attributes. Such methods are not applicable when we seek to detect conditional …
Interestingness measures provide information that can be used to prune or select association rules. A given value of an interestingness measure is often interpreted relative to the overall range of the values that the interestingness measure can take. However, properties of individual association rules restrict the val…