arXiv research

Unsupervised learning models can produce accurate but misleading predictions.

Unsupervised classification methods learn a discriminative classifier from unlabeled data, which has been proven to be an effective way of simultaneously clustering the data and training a classifier from the data. Various unsupervised classification methods obtain appealing results by the classifiers learned in an uns…

Minimal variations guide unsupervised learning for better downstream tasks.

Paper presents a workflow for reliable unsupervised learning in science.

We introduce a framework to leverage knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets. Our perspective avoids the subjectivity inherent in unsupervised learning by reducing it to supervised learning, and provides a principled way to evaluate unsupervised algorithm…

Enhanced BCPNN learns hidden representations without labels.

New framework uses unsupervised learning for efficient exploration in RL.

Paper summarizes unsupervised learning challenges for disentangled representations.

A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that can be used for more effective learning of downstream tasks from modest amounts of labeled data. Many prior unsupervised learning works aim to do so by developing proxy objectives based on reconstruction, disent…

The authors advocate for more rigorous unsupervised cross-lingual learning methods.

A new method generates meta-tasks using generative models for unsupervised learning.

A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. Typically, this involves minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations us…

This paper proves the theoretical advantage of unsupervised pretraining for machine learning tasks.

Recently, multilayer bootstrap network (MBN) has demonstrated promising performance in unsupervised dimensionality reduction. It can learn compact representations in standard data sets, i.e. MNIST and RCV1. However, as a bootstrap method, the prediction complexity of MBN is high. In this paper, we propose an unsupervis…

New unsupervised learning technique learns independent kernels for better machine learning tasks.

An unsupervised deep learning method solves PIDEs for option pricing.

This paper develops a federated EM algorithm for unsupervised learning of mixture models.

Unsupervised contrastive learning has gained increasing attention in the latest research and has proven to be a powerful method for learning representations from unlabeled data. However, little theoretical analysis was known for this framework. In this paper, we study the optimization of deep unsupervised contrastive l…

This research shows unsupervised GANs can perform object segmentation without labels.

UDA learns target domain from unlabeled data via source knowledge transfer.

New method embeds phylogenetic trees for clustering, recovering evolutionary relationships.

Study examines the training process of an unsupervised learning model for detecting gravitational-wave transient noise.

Learning by children and animals occurs effortlessly and largely without obvious supervision. Successes in automating supervised learning have not translated to the more ambiguous realm of unsupervised learning where goals and labels are not provided. Barlow (1961) suggested that the signal that brains leverage for uns…

Paper proposes a novel unsupervised feature selection method using K-means and ADMM.

Many important classification problems, such as object classification, speech recognition, and machine translation, have been tackled by the supervised learning paradigm in the past, where training corpora of parallel input-output pairs are required with high cost. To remove the need for the parallel training corpora h…

The paper shows how integrating categorical semantics can enhance unsupervised domain translation.

The paper introduces metrics for robust unsupervised learning of vehicle interactions.

New method shows supervised learning can mimic unsupervised learning effectively.

Paper explores unsupervised learning for ultrasound image artifact removal.

Unsupervised feature learning has shown impressive results for a wide range of input modalities, in particular for object classification tasks in computer vision. Using a large amount of unlabeled data, unsupervised feature learning methods are utilized to construct high-level representations that are discriminative en…

CoDAG combines domain adaptation and generalization for unsupervised continual domain shift learning.

This paper addresses the problem of unsupervised clustering which remains one of the most fundamental challenges in machine learning and artificial intelligence. We propose the clustered generator model for clustering which contains both continuous and discrete latent variables. Discrete latent variables model the clus…

Unsupervised anomaly detection from high dimensional data like mobility networks is a challenging task. Study of different approaches of feature engineering from such high dimensional data have been a focus of research in this field. This study aims to investigate the transferability of features learned by network clas…

Ensemble learning, the machine learning paradigm where multiple algorithms are combined, has exhibited promising perfomance in a variety of tasks. The present work focuses on unsupervised ensemble classification. The term unsupervised refers to the ensemble combiner who has no knowledge of the ground-truth labels that …

A new deep learning framework selects representative samples for unsupervised learning.

Improved unsupervised anomaly detection using Random Forest.

Unsupervised learning is a discipline of machine learning which aims at discovering patterns in big data sets or classifying the data into several categories without being trained explicitly. We show that unsupervised learning techniques can be readily used to identify phases and phases transitions of many body systems…

A novel algorithm for unsupervised graph representation learning combining coarsening and mutual information maximization.

Unsupervised segmentation learns features without labels, improving accuracy.

Unsupervised learning representations generalize better than supervised learning under distribution shifts.

This paper proposes a new method for automatically selecting the optimal kernel bandwidth in density estimation.

In this work, we investigate unsupervised representation learning on medical time series, which bears the promise of leveraging copious amounts of existing unlabeled data in order to eventually assist clinical decision making. By evaluating on the prediction of clinically relevant outcomes, we show that in a practical …

New model combines ICA and HMM for unsupervised learning of nonstationary time series.

Unsupervised learning models can be indistinguishable without identifiability, leading to unreliable representations.

Minimalistic unsupervised learning with sparse manifold transform achieves SOTA performance.

Paper addresses unsupervised learning from incomplete measurements in inverse problems.

This paper critically examines unsupervised disentangled representation learning, revealing challenges and limitations.

Is all of machine learning supervised to some degree? The field of machine learning has traditionally been categorized pedagogically into $supervised~vs~unsupervised~learning$; where supervised learning has typically referred to learning from labeled data, while unsupervised learning has typically referred to learning …