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.
Clustered attention improves transformer efficiency for large sequences.
ABC learns context-aware representations for clustering.
POTA improves short text clustering by generating reliable pseudo-labels.
Transformers can cluster data from Gaussian mixtures without supervision.
Centroid Transformers reduce memory and computation by summarizing inputs into centroids.
Transformers cluster meaningless words around leaders for sentiment analysis.
Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compact graph embedding, upon which classic clustering methods like k-means or spectral clustering algorithms are applied. These two-step framewo…
Particles representing tokens cluster in Transformers, influenced by initial tokens and matrix spectrum.
Cluster analysis aims at separating patients into phenotypically heterogenous groups and defining therapeutically homogeneous patient subclasses. It is an important approach in data-driven disease classification and subtyping. Acute coronary syndrome (ACS) is a syndrome due to sudden decrease of coronary artery blood f…
The team predicts foreign exchange rates using clustering and attention models.
Clustering is one of the most important unsupervised problems in machine learning and statistics. Among many existing algorithms, kernel k-means has drawn much research attention due to its ability to find non-linear cluster boundaries and its inherent simplicity. There are two main approaches for kernel k-means: SVD o…
Paper proposes new methods for improving interatomic potentials.
A panoply of multi-view clustering algorithms has been developed to deal with prevalent multi-view data. Among them, spectral clustering-based methods have drawn much attention and demonstrated promising results recently. Despite progress, there are still two fundamental questions that stay unanswered to date. First, h…
Paper improves short text clustering by integrating semantic relationships into Optimal Transport.
Proposes SDCN to integrate structural information into deep clustering.
Transformers learn to cluster Gaussian mixtures as well as the EM algorithm.
Image clustering is an important but challenging task in machine learning. As in most image processing areas, the latest improvements came from models based on the deep learning approach. However, classical deep learning methods have problems to deal with spatial image transformations like scale and rotation. In this p…
Model-based clustering is a popular approach for clustering multivariate data which has seen applications in numerous fields. Nowadays, high-dimensional data are more and more common and the model-based clustering approach has adapted to deal with the increasing dimensionality. In particular, the development of variabl…
GAP learns node representations by attending to different parts of its neighborhood.
The clustering methods have recently absorbed even-increasing attention in learning and vision. Deep clustering combines embedding and clustering together to obtain optimal embedding subspace for clustering, which can be more effective compared with conventional clustering methods. In this paper, we propose a joint lea…
RTFN extracts robust temporal features for time series analysis.
Quantum computing for machine learning attracts increasing attention and recent technological developments suggest that especially adiabatic quantum computing may soon be of practical interest. In this paper, we therefore consider this paradigm and discuss how to adopt it to the problem of binary clustering. Numerical …
DEMVC improves multi-view clustering with collaborative training and deep autoencoders.
Convex clustering, a convex relaxation of k-means clustering and hierarchical clustering, has drawn recent attentions since it nicely addresses the instability issue of traditional nonconvex clustering methods. Although its computational and statistical properties have been recently studied, the performance of convex c…
The paper explores fair clustering, a niche area in machine learning.
Clustering on hypergraphs has been garnering increased attention with potential applications in network analysis, VLSI design and computer vision, among others. In this work, we generalize the framework of modularity maximization for clustering on hypergraphs. To this end, we introduce a hypergraph null model, analogou…
Proposes a new method for clustering tasks in multi-task learning.
In this paper we take a problem of unsupervised nodes clustering on graphs and show how recent advances in attention models can be applied successfully in a "hard" regime of the problem. We propose an unsupervised algorithm that encodes Bethe Hessian embeddings by optimizing soft modularity loss and argue that our mode…
Enhances robustness of multi-view clustering via partition fusion.
Multi-view clustering has received much attention recently. Most of the existing multi-view clustering methods only focus on one-sided clustering. As the co-occurring data elements involve the counts of sample-feature co-occurrences, it is more efficient to conduct two-sided clustering along the samples and features si…
Benchmark study evaluates 8 clustering methods on 99 UCR time series datasets.
Perfect clustering achieved in hypergraphs with enough interactions.
Community detection, which focuses on clustering nodes or detecting communities in (mostly) a single network, is a problem of considerable practical interest and has received a great deal of attention in the research community. While being able to cluster within a network is important, there are emerging needs to be ab…
Consensus clustering fuses diverse basic partitions (i.e., clustering results obtained from conventional clustering methods) into an integrated one, which has attracted increasing attention in both academic and industrial areas due to its robust and effective performance. Tremendous research efforts have been made to t…
New RESK distributions improve robust clustering of skewed data.
Interprets how intrinsic motivation shapes behavior in RL agents.
Even though clustering trajectory data attracted considerable attention in the last few years, most of prior work assumed that moving objects can move freely in an euclidean space and did not consider the eventual presence of an underlying road network and its influence on evaluating the similarity between trajectories…
A tutorial on various methods for clustering longitudinal data.
A new model for graph clustering using curvature spaces.
The clustering ensemble technique aims to combine multiple clusterings into a probably better and more robust clustering and has been receiving an increasing attention in recent years. There are mainly two aspects of limitations in the existing clustering ensemble approaches. Firstly, many approaches lack the ability t…
We consider the problem of community detection or clustering in the labeled Stochastic Block Model (LSBM) with a finite number of clusters of sizes linearly growing with the global population of items . Every pair of items is labeled independently at random, and label appears with probability $p(i,j,\ell)…
The paper simplifies Bayesian posterior using clustering to make inference more manageable.
New Ising models improve consensus clustering on specialized hardware.
This paper introduces GEMINI, a new metric for unsupervised neural network training that avoids the need for regularizations.
Adapts attention to supervised learning for personalized predictions.
Study shows how specialized attention circuits emerge during transformer training.
This paper introduces GEMINI, a new mutual information metric for unsupervised neural network training.
Multi-task clustering (MTC) has attracted a lot of research attentions in machine learning due to its ability in utilizing the relationship among different tasks. Despite the success of traditional MTC models, they are either easy to stuck into local optima, or sensitive to outliers and noisy data. To alleviate these p…