unsupervised-learning

Proposes the first unsupervised anomaly detection framework for IC latent defect screening using a Diffusion Transformer, achieving state-of-the-art performance on industrial 16nm test data.

SilIF augments Isolation Forest with a silhouette-based scoring layer on per-tree path length fingerprints, improving unsupervised transaction fraud detection on the IEEE-CIS benchmark by +0.0080 AUC-PR on average.

Proposes CALAD, a channel-aware contrastive learning framework for multivariate time series anomaly detection that uses estimated channel relevance to construct contrastive samples, achieving state-of-the-art performance.

A side project presents a Hebbian architecture AI model that avoids backpropagation and gradients, achieving 50 epochs on CIFAR-10 with emergent behaviors like accuracy dips followed by jumps and recovery after targeted damage.

This paper systematically studies how different evaluation objectives (accuracy, silhouette score, PCA reconstruction loss) and subset-size regularization directions affect search dynamics and solution quality in multiobjective unsupervised feature selection, showing that silhouette-based formulations bias toward trivial low-cardinality solutions while PCA loss yields compact subsets with competitive accuracy.

AdaGraph is a graph-native clustering algorithm that operates within the kNN graph topology to overcome the curse of dimensionality, validated across genomics, NLP, and materials science domains via the Structure-Centric Machine Learning paradigm.

This paper investigates whether fMRI representations from different subjects' visual cortices can be aligned using unsupervised geometric methods, finding evidence for approximately isometric structure across individuals, extending the Platonic Representation Hypothesis to human brains.

TILT introduces a novel objective for unsupervised domain adaptation under covariate shift that penalizes an auxiliary component on unlabeled target data, implicitly achieving self-localized importance weighting with bounded estimands. Theoretical guarantees and experiments on shifted CIFAR-100 show improved target performance over baselines.

This paper introduces an unsupervised framework for modeling acquisition-related variability in structural connectomes using hybrid latent space modeling, eliminating the need for manual capacity tuning by architecturally annealing encoder outputs.

This paper proposes a locality-aware private class identification approach and a reliable optimal transport-based method (ReOT) to address domain adaptation challenges under extreme label shift, particularly distinguishing shared from private classes.

This paper introduces MultiLinguahah, an unsupervised multilingual method for acoustic laughter segmentation using Isolation Forests on BYOL-A encoder representations. The authors demonstrate that their approach outperforms state-of-the-art supervised methods in non-English settings by treating laughter detection as an anomaly detection task.

MSR-MEL introduces an unsupervised framework using LLMs to synthesize and reason over multi-perspective evidence for multimodal entity linking, outperforming prior methods on standard benchmarks.

Researchers from the University of British Columbia propose an unsupervised graph-based system for organizing arguments from online debates by constructing interaction graphs and applying community detection to reveal diverse viewpoint distributions. The approach requires no training data and aims to help users navigate complex argumentative landscapes and combat filter bubbles.

C-Mining proposes an unsupervised framework for discovering cultural seeds in LLM training data by exploiting cross-lingual geometric misalignment in embedding spaces, enabling scalable synthetic data generation for cultural alignment without manual or LLM supervision.

The paper introduces diverse dictionary learning, showing that key set-theoretic relationships among latent variables can be identified from observational data without strong assumptions, enabling partial or full identifiability with minimal inductive bias.

OpenAI presents a contrastive pre-training approach for generating high-quality text and code embeddings at scale without supervision, achieving state-of-the-art results on linear-probe classification, semantic search, and code search benchmarks.

OpenAI's Image GPT (iGPT) applies GPT-2 transformers to pixel sequences for image generation and classification, demonstrating that the same architecture used for language can learn coherent visual features in an unsupervised manner and achieve competitive performance on image classification benchmarks.

OpenAI released MuseNet, a deep neural network based on GPT-2 architecture that generates 4-minute musical compositions with 10 instruments by learning patterns from hundreds of thousands of MIDI files. The model can combine multiple music styles and blend them in novel ways.

OpenAI introduces GPT-2, a 1.5 billion parameter transformer-based language model trained on 40GB of internet text that achieves state-of-the-art performance on language modeling benchmarks and demonstrates zero-shot capabilities in reading comprehension, translation, question answering, and summarization. Due to safety concerns, only a smaller model and technical paper are released publicly rather than the full trained model.

OpenAI presents a two-stage approach for improving language understanding: pretraining a transformer model on large unsupervised datasets using language modeling, then fine-tuning on smaller supervised datasets for specific tasks. The method achieves state-of-the-art results across diverse tasks including commonsense reasoning, semantic similarity, and reading comprehension with minimal hyperparameter tuning.