arXiv research

Posterior regularization enhances Bayesian hierarchical mixture clustering by improving node separation.

A new method for blind source separation using hierarchical structure and KL divergence.

Logarithmic separation profile in hyperbolic groups shows hierarchical structure.

New algorithm proves deep networks can learn better than shallow ones.

New approach for classification using trigonometric polynomial kernels from signal processing.

Paper tackles online hierarchical clustering, offering efficient algorithms with good quality clusters.

ANTs integrate neural networks and decision trees for better performance.

New Bayesian method for joint sparse parameter inference.

New criteria for relative hyperbolicity in hierarchically hyperbolic spaces.

Paper proposes a new metric learning method for better class separability.

Hierarchical clustering is a popular method for analyzing data which associates a tree to a dataset. Hartigan consistency has been used extensively as a framework to analyze such clustering algorithms from a statistical point of view. Still, as we show in the paper, a tree which is Hartigan consistent with a given dens…

MSOL learns hierarchical policies for multitask tasks with soft options.

We prove a geometric model for HHS hierarchies as CAT(0) cube complexes.

HiDe learns hierarchical control for complex tasks by separating planning and control.

The study shows that certain curve graphs are hierarchically hyperbolic but not Gromov hyperbolic.

Hyperbolic space outperforms Euclidean in learning hierarchical data.

The paper explores clustering methods using Bregman divergences.

Proves deep networks can learn hierarchical structures efficiently.

This paper tackles multilingual speech processing by optimizing conflicting objectives hierarchically.

New method improves inference for hierarchical models.

In image classification, visual separability between different object categories is highly uneven, and some categories are more difficult to distinguish than others. Such difficult categories demand more dedicated classifiers. However, existing deep convolutional neural networks (CNN) are trained as flat N-way classifi…

Grinch efficiently clusters large datasets with complex structures.

Graph Pointer Networks and hierarchical reinforcement learning solve combinatorial optimization problems like TSP.

BoRA finetunes multi-task LLMs by sharing information through hierarchical priors.

Improved image reconstruction and anomaly detection using hierarchical VAEs.

Randomized hierarchical clustering tests for stability and detects clusters.

Due to myriads of classes, designing accurate and efficient classifiers becomes very challenging for multi-class classification. Recent research has shown that class structure learning can greatly facilitate multi-class learning. In this paper, we propose a novel method to learn the class structure for multi-class clas…

New algorithm learns sub-task policies from unsegmented demonstrations.

UNTIE learns representations of coupled categorical data.

We derive a statistical model for estimation of a dendrogram from single linkage hierarchical clustering (SLHC) that takes account of uncertainty through noise or corruption in the measurements of separation of data. Our focus is on just the estimation of the hierarchy of partitions afforded by the dendrogram, rather t…

We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Using hierarchical matrix approximations for the kernel matrix the memory requirements, the number of floating point operations, and the execution time are drastically reduced compared to standard dense linear algebra routi…

New WildMix dataset and Spectro-Temporal Transformer model for better monoaural audio source separation.

Deep convolutional networks provide state of the art classifications and regressions results over many high-dimensional problems. We review their architecture, which scatters data with a cascade of linear filter weights and non-linearities. A mathematical framework is introduced to analyze their properties. Computation…

A novel hierarchical Bayesian approach to Federated Learning reduces data exposure and improves convergence rates.

DHGAK aligns substructures for better graph kernel performance.

A new model separates persistence and transition priors in HDP-HMM.

A hierarchical routing mixture of experts model for complex regression tasks.

New method improves uncertainty calibration in deep learning.

FL+HC improves federated learning on non-iid data by clustering local updates.

RG-Flow combines RG and sparse priors for hierarchical image disentanglement.

A hierarchical community detection method using recursive partitioning.

Proposes selective inference for testing differences in means between clusters.

HPPCA improves imputation of longitudinal data with missing values.

New criterion assesses cluster separability for validation.

A hierarchical model shows how scaling laws emerge from sequential feature recovery.

BiGraphNet generalizes graph neural networks for more efficient operations.

New model shows hierarchical proof structure helps theorem provers.

An empirical analysis of interest rates in money and capital markets is performed. We investigate a set of 34 different weekly interest rate time series during a time period of 16 years between 1982 and 1997. Our study is focused on the collective behavior of the stochastic fluctuations of these time-series which is in…