·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Effects of sparsity and superposition on loss in simple autoencoders6h◆Bridging Data Gaps in Structural Fragility Modeling through Transfer Learning: Methodology and Case Studies6h◆Ensuring Trustworthy Online A/B Testing: Addressing Five Key Questions on CUPED6h◆Beyond AHI: An Interpretable Causal-Discovery-Guided Framework for Sleep Recovery in Connected Health6h◆Hierarchical Attention via Domain Decomposition6h◆Do as the Romans Do: Learning Universal Behaviors from Heterogeneous Agents6h◆TS-Fault: Benchmarking Time Series Forecasters Against Structural Faults6h◆Semantic Robustness Certification for Vision-Language Models6h◆Geometric and Stochastic Analysis of Discontinuities in Sparse Mixture-of-Experts6h◆Smoothness-Based Derandomization of PAC-Bayes Bounds6h◆JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling6h◆Seeing Before Reasoning: Decoupling Perception and Reasoning for Shortcut-Resilient Multimodal On-Policy Self-Distillation6h◆INDEQS: Informed Neural controlled Differential EQuationS6h◆ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysis6h◆The Reward Was in Your Data All Along: Correcting Flow Matching with Discriminator-Guided RL6h◆A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Development6h◆SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering6h◆Risk Stratification for ICU Delirium using Pervasive Ambient Sensing Information6h◆Does VLA Even Know the Basics? Measuring Commonsense and World Knowledge Retention in Vision-Language-Action Models6h◆P-K-GCN: Physics-augmented Koopman-enhanced Graph Convolutional Network for Deep Spatiotemporal Super-resolution6h◆Effects of sparsity and superposition on loss in simple autoencoders6h◆Bridging Data Gaps in Structural Fragility Modeling through Transfer Learning: Methodology and Case Studies6h◆Ensuring Trustworthy Online A/B Testing: Addressing Five Key Questions on CUPED6h◆Beyond AHI: An Interpretable Causal-Discovery-Guided Framework for Sleep Recovery in Connected Health6h◆Hierarchical Attention via Domain Decomposition6h◆Do as the Romans Do: Learning Universal Behaviors from Heterogeneous Agents6h◆TS-Fault: Benchmarking Time Series Forecasters Against Structural Faults6h◆Semantic Robustness Certification for Vision-Language Models6h◆Geometric and Stochastic Analysis of Discontinuities in Sparse Mixture-of-Experts6h◆Smoothness-Based Derandomization of PAC-Bayes Bounds6h◆JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling6h◆Seeing Before Reasoning: Decoupling Perception and Reasoning for Shortcut-Resilient Multimodal On-Policy Self-Distillation6h◆INDEQS: Informed Neural controlled Differential EQuationS6h◆ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysis6h◆The Reward Was in Your Data All Along: Correcting Flow Matching with Discriminator-Guided RL6h◆A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Development6h◆SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering6h◆Risk Stratification for ICU Delirium using Pervasive Ambient Sensing Information6h◆Does VLA Even Know the Basics? Measuring Commonsense and World Knowledge Retention in Vision-Language-Action Models6h◆P-K-GCN: Physics-augmented Koopman-enhanced Graph Convolutional Network for Deep Spatiotemporal Super-resolution6h◆
News/Seed-Guided Semi-Supervised Clustering by A-Contrario Anomaly Detection
arxiv
PublishedJune 18, 2026 at 4:00 AM

Seed-Guided Semi-Supervised Clustering by A-Contrario Anomaly Detection

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.18833v1 Announce Type: new Abstract: This paper introduces a semi-supervised clustering framework grounded in the statistical duality between grouping principles and anomaly detection. We address the challenge of robust cluster definition in noisy environments -- a task where partitioning

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivEffects of sparsity and superposition on loss in simple autoencoders6harxivBridging Data Gaps in Structural Fragility Modeling through Transfer Learning: Methodology and Case Studies6harxivEnsuring Trustworthy Online A/B Testing: Addressing Five Key Questions on CUPED6harxivBeyond AHI: An Interpretable Causal-Discovery-Guided Framework for Sleep Recovery in Connected Health6h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews