Papers Clustering
“Clustering” 태그가 달린 논문 10,718편 · 필터 해제
Tri-Learn Graph Fusion Network for Attributed Graph Clustering
In recent years, models based on Graph Convolutional Networks (GCN) have made significant strides in the field of graph data analysis. However, challenges such as over-smoothing and over-compression remain when handling …
ClusteringDeep ClusteringGraph ClusteringNews ClassificationRanking Vectors Clustering: Theory and Applications
We study the problem of clustering ranking vectors, where each vector represents preferences as an ordered list of distinct integers. Specifically, we focus on the k-centroids ranking vectors clustering problem (KRC), wh…
ClusteringCar Object Counting and Position Estimation via Extension of the CLIP-EBC Framework
In this paper, we investigate the applicability of the CLIP-EBC framework, originally designed for crowd counting, to car object counting using the CARPK dataset. Experimental results show that our model achieves second-…
ClusteringCrowd CountingObjectObject Counting+1GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning
Microscopic assessment of histopathology images is vital for accurate cancer diagnosis and treatment. Whole Slide Image (WSI) classification and captioning have become crucial tasks in computer-aided pathology. However, …
Caption GenerationClusteringGraph Neural Networkimage-classification+3Consistency and Inconsistency in $K$-Means Clustering
A celebrated result of Pollard proves asymptotic consistency for $k$-means clustering when the population distribution has finite variance. In this work, we point out that the population-level $k$-means clustering proble…
ClusteringMC-INR: Efficient Encoding of Multivariate Scientific Simulation Data using Meta-Learning and Clustered Implicit Neural Representations
Implicit Neural Representations (INRs) are widely used to encode data as continuous functions, enabling the visualization of large-scale multivariate scientific simulation data with reduced memory usage. However, existin…
ClusteringMeta-LearningSupercm: Revisiting Clustering for Semi-Supervised Learning
The development of semi-supervised learning (SSL) has in recent years largely focused on the development of new consistency regularization or entropy minimization approaches, often resulting in models with complex traini…
ClusteringTemporal Rate Reduction Clustering for Human Motion Segmentation
Human Motion Segmentation (HMS), which aims to partition videos into non-overlapping human motions, has attracted increasing research attention recently. Existing approaches for HMS are mainly dominated by subspace clust…
ClusteringMotion SegmentationAnalytic inference with two-way clustering
This paper studies analytic inference along two dimensions of clustering. In such setups, the commonly used approach has two drawbacks. First, the corresponding variance estimator is not necessarily positive. Second, inf…
ClusteringHERCULES: Hierarchical Embedding-based Recursive Clustering Using LLMs for Efficient Summarization
The explosive growth of complex datasets across various modalities necessitates advanced analytical tools that not only group data effectively but also provide human-understandable insights into the discovered structures…
ClusteringThe Shape of Consumer Behavior: A Symbolic and Topological Analysis of Time Series
Understanding temporal patterns in online search behavior is crucial for real-time marketing and trend forecasting. Google Trends offers a rich proxy for public interest, yet the high dimensionality and noise of its time…
ClusteringMarketingTime SeriesTopological Data AnalysisA Study of Dynamic Stock Relationship Modeling and S&P500 Price Forecasting Based on Differential Graph Transformer
Stock price prediction is vital for investment decisions and risk management, yet remains challenging due to markets' nonlinear dynamics and time-varying inter-stock correlations. Traditional static-correlation models fa…
ClusteringStock Price PredictionTime Series PredictionMiCo: Multiple Instance Learning with Context-Aware Clustering for Whole Slide Image Analysis
Multiple instance learning (MIL) has shown significant promise in histopathology whole slide image (WSI) analysis for cancer diagnosis and prognosis. However, the inherent spatial heterogeneity of WSIs presents critical …
ClusteringMultiple Instance LearningPrognosisPrivate Training & Data Generation by Clustering Embeddings
Deep neural networks often use large, high-quality datasets to achieve high performance on many machine learning tasks. When training involves potentially sensitive data, this process can raise privacy concerns, as large…
ClusteringLearning Augmented Graph $k$-Clustering
Clustering is a fundamental task in unsupervised learning. Previous research has focused on learning-augmented $k$-means in Euclidean metrics, limiting its applicability to complex data representations. In this paper, we…
ClusteringComputational lower bounds in latent models: clustering, sparse-clustering, biclustering
In many high-dimensional problems, like sparse-PCA, planted clique, or clustering, the best known algorithms with polynomial time complexity fail to reach the statistical performance provably achievable by algorithms fre…
ClusteringFrom Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots
Achieving general agile whole-body control on humanoid robots remains a major challenge due to diverse motion demands and data conflicts. While existing frameworks excel in training single motion-specific policies, they …
ClusteringRevisiting Clustering of Neural Bandits: Selective Reinitialization for Mitigating Loss of Plasticity
Clustering of Bandits (CB) methods enhance sequential decision-making by grouping bandits into clusters based on similarity and incorporating cluster-level contextual information, demonstrating effectiveness and adaptabi…
Change DetectionClusteringSequential Decision MakingEfficient Long-Context LLM Inference via KV Cache Clustering
Large language models (LLMs) with extended context windows have become increasingly prevalent for tackling complex tasks. However, the substantial Key-Value (KV) cache required for long-context LLMs poses significant dep…
ClusteringSelf-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation
Self-supervised learning (SSL) has achieved major advances in natural images and video understanding, but challenges remain in domains like echocardiography (heart ultrasound) due to subtle anatomical structures, complex…
Anomaly DetectionClusteringRepresentation LearningSelf-Supervised Learning+2