A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Advances citation and subject label recommendation using multi-modal adversarial autoencoders.
This paper reviews PU learning evaluation methods and provides practical recommendations.
The paper identifies and analyzes subjective class issues in user-generated data.
Self-supervised method improves biosignal models with limited labeled data and subjects.
The paper addresses treatment recommendation problems by optimizing distributional characteristics.
Paper compares NMF and LDA for topic labeling in customer communications.
This book is a textbook for the basic course of differential geometry. It is recommended as an introductory material for this subject.
The profusion of online news articles makes it difficult to find interesting articles, a problem that can be assuaged by using a recommender system to bring the most relevant news stories to readers. However, news recommendation is challenging because the most relevant articles are often new content seen by few users. …
SCR diversifies recommendations by applying style transfer to user profiles.
The need for diversification of recommendation lists manifests in a number of recommender systems use cases. However, an increase in diversity may undermine the utility of the recommendations, as relevant items in the list may be replaced by more diverse ones. In this work we propose a novel method for maximizing the u…
We propose a comprehensive end-to-end pipeline for Twitter hashtags recommendation system including data collection, supervised training setting and zero shot training setting. In the supervised training setting, we have proposed and compared the performance of various deep learning architectures, namely Convolutional …
A substantial progress in development of new and efficient tensor factorization techniques has led to an extensive research of their applicability in recommender systems field. Tensor-based recommender models push the boundaries of traditional collaborative filtering techniques by taking into account a multifaceted nat…
Develops NFCF to reduce gender bias in social media recommendation systems.
Proposes dynamic model type recommendation for OLP technique.
Knowledge graphs capture structured information and relations between a set of entities or items. As such knowledge graphs represent an attractive source of information that could help improve recommender systems. However, existing approaches in this domain rely on manual feature engineering and do not allow for an end…
Supervisory signals can help topic models discover low-dimensional data representations that are more interpretable for clinical tasks. We propose a framework for training supervised latent Dirichlet allocation that balances two goals: faithful generative explanations of high-dimensional data and accurate prediction of…
Industrial recommender systems deal with extremely large action spaces -- many millions of items to recommend. Moreover, they need to serve billions of users, who are unique at any point in time, making a complex user state space. Luckily, huge quantities of logged implicit feedback (e.g., user clicks, dwell time) are …
A new method uses counterfactual learning to improve recommendation system evaluation.
Adaptive sampler improves recommendation for implicit feedback data.
CGM combines SSL and LFM for better recommendation performance.
Recommender systems have been widely adopted by electronic commerce and entertainment industries for individualized prediction and recommendation, which benefit consumers and improve business intelligence. In this article, we propose an innovative method, namely the recommendation engine of multilayers (REM), for tenso…
The paper improves recommendation systems by ensuring their outputs are reliable.
The paper briefly introduces multiple classifier systems and describes a new algorithm, which improves classification accuracy by means of recommendation of a proper algorithm to an object classification. This recommendation is done assuming that a classifier is likely to predict the label of the object correctly if it…
Information systems experience an ever-growing volume of unstructured data, particularly in the form of textual materials. This represents a rich source of information from which one can create value for people, organizations and businesses. For instance, recommender systems can benefit from automatically understanding…
Heart rate estimation from electrocardiogram signals is very important for the early detection of cardiovascular diseases. However, due to large individual differences and varying electrocardiogram signal quality, there does not exist a single reliable estimation algorithm that works well on all subjects. Every algorit…
A new PLL method uses class activation values to improve robustness.
XR-Transformer accelerates XMC by recursively fine-tuning on multi-resolution objectives.
Inter-subject variability between individuals poses a challenge in inter-subject brain signal analysis problems. A new algorithm for subject-selection based on clustering covariance matrices on a Riemannian manifold is proposed. After unsupervised selection of the subsets of relevant subjects, data in a cluster is mapp…
Modern computing and communication technologies can make data collection procedures very efficient. However, our ability to analyze large data sets and/or to extract information out from them is hard-pressed to keep up with our capacities for data collection. Among these huge data sets, some of them are not collected f…
Bayesian framework evaluates predictors of subjective visual tasks.
The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy. Artist labels are less subjective and less noisy, while certain artists may relate more strongly to certain genres. At the same time, at prediction time, it is not guaranteed that artist …
RecoBERT uses a language model to recommend items from catalogs.
This paper improves attacks on recommender systems by solving optimization problems more precisely.
Proposes a comprehensive framework for financial product lead recommendations using graph representation learning and link prediction.
Efficiently classifies binary labels with XOR queries, even under noisy conditions.
AutoTransfer improves subject transfer learning for biosignal datasets.
New metrics needed for streaming ML due to delayed labels.
NIS learns optimal embedding sizes for recommendation models.
Recommendation algorithms are widely adopted in marketplaces to help users find the items they are looking for. The sparsity of the items by user matrix and the cold-start issue in marketplaces pose challenges for the off-the-shelf matrix factorization based recommender systems. To understand user intent and tailor rec…
Survival analysis has been developed and applied in the number of areas including manufacturing, finance, economics and healthcare. In healthcare domain, usually clinical data are high-dimensional, sparse and complex and sometimes there exists few amount of time-to-event (labeled) instances. Therefore building an accur…
A new method selects the best feature selection technique for datasets.
Decision makers, such as doctors and judges, make crucial decisions such as recommending treatments to patients, and granting bails to defendants on a daily basis. Such decisions typically involve weighting the potential benefits of taking an action against the costs involved. In this work, we aim to automate this task…
New system assigns vehicles to routes for cost and energy efficiency.
SAFER improves personalized treatment recommendations for dynamic clinical contexts.
System exposes study population descriptions in clinical guidelines.
Spotlight method finds hidden errors in deep learning models.
We study the problem of aggregation noisy labels. Usually, it is solved by proposing a stochastic model for the process of generating noisy labels and then estimating the model parameters using the observed noisy labels. A traditional assumption underlying previously introduced generative models is that each object has…
New method debiases selection bias in PU classification with exposure data.