unsupervised-learning

FB-GDM introduces a fully-Bayesian guided diffusion method for high-dimensional linear inverse problems, eliminating per-task hyperparameter tuning and demonstrating robust performance improvements over existing techniques.

This paper analyzes failures in reconstruction-based unsupervised learning through geometric conditions and proposes new methods like Dynamic Push and Pull to enhance anomaly detection performance.

This paper introduces sheaf regularization to stabilize Decentralized SyncMap for unsupervised continual chunking, achieving higher normalized mutual information and better adaptation to input distribution shifts.

The paper proposes UGCAD, an unsupervised framework using variational graph autoencoders and clustering to detect collective anomalies in IoT network traffic, with experiments on benchmark datasets showing its effectiveness.

This paper introduces an unsupervised, training-free approach to discover prompt-conditional stylistic axes in LLM hidden activations using sampling and PCA, validated through human studies.

DSD is a diffusion-based method for discovering diverse and reusable motor skills in simulated humanoid control, improving upon prior skill discovery techniques with broader behavioral coverage.

This paper presents an unsupervised graph neural network framework for solving the Minimum Dominating Set problem, achieving significant speed improvements and generalization for influence maximization in social networks.

This paper uses 1,744 Landsat and Sentinel-2 satellite images of Robert Smithson's Spiral Jetty to compute multi-feature complexity signatures, revealing that image complexity acts as a leading indicator of lake elevation and climate metrics over 40 years.

This paper shows that hierarchical clustering can simultaneously satisfy scale invariance, richness, and consistency axioms, resolving Kleinberg's Impossibility Theorem for flat clustering. It constructs admissible hierarchical methods and analyzes their diversity and common backbone.

This paper proposes a graph dictionary learning framework that represents graphs as Gaussian distributions using filtered Laplacians and optimal transport distances, achieving competitive performance in graph clustering and classification tasks.

This paper proposes an unsupervised framework combining manifold learning and interpretable graph embeddings to address geometric and interpretability gaps in visual representations, enhancing performance in image retrieval and GCN classification tasks.

This paper introduces GRaCE, an unsupervised framework for generating interpretable graph embeddings using rank-based measures, which outperforms existing methods in retrieval, classification, and clustering tasks on textual and image data.

GUIDE is a generative unsupervised framework for Chinese query correction that uses phonetic and visual shared-ID encoding to constrain corrections and adapt to changing vocabularies, outperforming baselines in experiments and online A/B testing.

This survey paper categorizes 80 unsupervised post-training methods for foundation models, organizing them by the internal update signals and presenting a unified framework for selection and evaluation.

This paper investigates unsupervised anomaly detection using flow matching on tabular data, focusing on contaminated training sets and comparing different scoring methods for robustness.

Co-RL is a cooperative multi-agent framework that enables unsupervised reasoning improvement in language and vision-language models by using peer-derived rewards, reducing reliance on ground-truth labels and mitigating training collapse through increased cohort diversity.

The paper proposes CMR-Mamba, a causal mechanism monitoring method using Mamba state-space models for unsupervised industrial fault detection, focusing on coupling faults and evaluated on electromechanical, hydraulic, and cyber-physical systems.

Proposes a dual-primal graph VAE architecture that treats ground-truth labels as latent variables to aggregate noisy crowdsourced labels, achieving state-of-the-art results on crowdsourcing benchmarks without needing a separate classifier.

Presents STCAD, a scalable framework using BERT-based encoding and CURE clustering to perform trajectory clustering and anomaly detection on terabyte-scale AIS maritime data, demonstrating stable clusters and clear separation of anomalous vessel behavior.

This paper proposes an unsupervised fire-zone segmentation method combining watershed detection with K-means clustering to improve short-term wildfire prediction, showing consistent gains over grid-based approaches across multiple French departments and forecasting models.