Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines
As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …
systems continues to generate massive amounts of data. Many approaches have been …
A brief review of portfolio optimization techniques
Portfolio optimization has always been a challenging proposition in finance and
management. Portfolio optimization facilitates in selection of portfolios in a volatile market …
management. Portfolio optimization facilitates in selection of portfolios in a volatile market …
[BOOK][B] Data clustering: theory, algorithms, and applications
The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …
2007. Starting with the common ground and knowledge for data clustering, the monograph …
Molecular sets (MOSES): a benchmarking platform for molecular generation models
Generative models are becoming a tool of choice for exploring the molecular space. These
models learn on a large training dataset and produce novel molecular structures with similar …
models learn on a large training dataset and produce novel molecular structures with similar …
[HTML][HTML] How much can k-means be improved by using better initialization and repeats?
In this paper, we study what are the most important factors that deteriorate the performance
of the k-means algorithm, and how much this deterioration can be overcome either by using …
of the k-means algorithm, and how much this deterioration can be overcome either by using …
Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization
In this paper, we propose a new clustering model, called DEeP Embedded RegularIzed
ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace …
ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace …
Making ai forget you: Data deletion in machine learning
Intense recent discussions have focused on how to provide individuals with control over
when their data can and cannot be used---the EU's Right To Be Forgotten regulation is an …
when their data can and cannot be used---the EU's Right To Be Forgotten regulation is an …
Heterogeneity for the win: One-shot federated clustering
In this work, we explore the unique challenges—and opportunities—of unsupervised
federated learning (FL). We develop and analyze a one-shot federated clustering scheme …
federated learning (FL). We develop and analyze a one-shot federated clustering scheme …
[BOOK][B] Modern algorithms of cluster analysis
ST Wierzchoń, MA Kłopotek - 2018 - Springer
This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …
interested in cluster analysis, lists major application areas, basic theoretical and practical …
Diverse mini-batch active learning
F Zhdanov - arxiv preprint arxiv:1901.05954, 2019 - arxiv.org
We study the problem of reducing the amount of labeled training data required to train
supervised classification models. We approach it by leveraging Active Learning, through …
supervised classification models. We approach it by leveraging Active Learning, through …