Search · four archives
Search · four archives
20 papers · ranked by Valyu relevance
David B. Dahl, Devin J. Johnson, R. Jacob Andros
Feature allocation models postulate a sampling distribution whose parameters are derived from shared features. Bayesian models place a prior distribution on the feature allocation, and Markov chain Monte Carlo is typically used for model fitting, which results in thousands of feature allocations sampled from the…
Lorenzo Ghilotti, Federico Camerlenghi, Tommaso Rigon
Feature allocation models are an extension of Bayesian nonparametric clustering models, where individuals can share multiple features. We study a broad class of models whose probability distribution has a product form, which includes the popular Indian buffet process. This class plays a prominent role among existing…
Mario Beraha, Federico Camerlenghi, Lorenzo Ghilotti
allocation models Authors: ['Mario Beraha' 'Federico Camerlenghi' 'Lorenzo Ghilotti'] We introduce and study a unified Bayesian framework for extended feature allocations which flexibly captures interactions – such as repulsion or attraction – among features and their associated weights. We provide a complete Bayesian…
Ghilotti, Lorenzo, Camerlenghi, Federico + 4 more
Feature and trait allocation models are fundamental objects in Bayesian nonparametrics and play a prominent role in several applications. Existing approaches, however, typically assume full exchangeability of the data, which may be restrictive in settings characterized by heterogeneous but related groups. In this…
Aleksandra Vatian, Natalia Gusarova, Ivan Tomilov, Wei Li
In the modern world, there is a need to provide a better understanding of the importance or relevance of the available descriptive features for predicting target attributes to solve the feature ranking problem. Among the published works, the vast majority are devoted to the problems of feature selection and extraction…
Biao Zhang, Ying Zhang, Xuchu Jiang
Ozone is one of the most important air pollutants, with significant impacts on human health, regional air quality and ecosystems. In this study, we use geographic information and environmental information of the monitoring site of 5577 regions in the world from 2010 to 2014 as feature input to predict the long-term…
Kaixin Yang, Long Liu, Yalu Wen
Feature selection is an indispensable step for the analysis of high-dimensional molecular data. Despite its importance, consensus is lacking on how to choose the most appropriate feature selection methods, especially when the performance of the feature selection methods itself depends on hyper-parameters. Bayesian…
Soroosh Shalileh, Dmitry Ignatov, Anastasiya Lopukhina, Olga Dragoy + 1 more
'Mohammad Amin Fraiwan'] This paper represents our research results in the pursuit of the following objectives: (i) to introduce a novel multi-sources data set to tackle the shortcomings of the previous data sets, (ii) to propose a robust artificial intelligence-based solution to identify dyslexia in primary school…
Owen Forbes, Edgar Santos-Fernandez, Paul Pao-Yen Wu, Hong-Bo Xie + 7 more
'Paul E. Schwenn' 'Jim Lagopoulos' 'Lia Mills' 'Dashiell D. Sacks' 'Daniel F. Hermens' 'Kerrie Mengersen' 'Dariusz Siudak'] Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature. The approach of reporting results…
Anna Jenul, Stefan Schrunner, Jürgen Pilz, Oliver Tomić
Feature selection represents a measure to reduce the complexity of high-dimensional datasets and gain insights into the systematic variation in the data. This aspect is of specific importance in domains that rely on model interpretability, such as life sciences. We propose UBayFS, an ensemble feature selection…
Siying Li, Carol A. Seger, Meng Liu, Wenshan Dong + 2 more
In a dynamic environment, expectations of the future constantly change based on updated evidence and affect the dynamic allocation of attentional resources.To further investigate the neural mechanisms underlying efficient allocation of attention, we employed a modified Central Cue Posner paradigm in which the…
Claus Metzner, Achim Schilling, Maximilian Traxdorf, Konstantin Tziridis + 3 more
'Konstantin Tziridis' 'Andreas Maier' 'Holger Schulze' 'Patrick Krauss'] Data classification, the process of analyzing data and organizing it into categories or clusters, is a fundamental computing task of natural and artificial information processing systems. Both supervised classification and unsupervised clustering…
Jonas Verhellen
In recent years, there have been considerable academic and industrial research efforts to develop novel generative models for high-performing, small molecules. Traditional, rules-based algorithms such as genetic algorithms [Jensen, Chem. Sci., 2019, 12, 3567-3572] have, however, been shown to rival deep learning…
Joram Soch, Carsten Allefeld
We propose the statistical modelling approach to supervised learning (i.e. predicting labels from features) as an alternative to algorithmic machine learning (ML). The approach is demonstrated by employing a multivariate general linear model (MGLM) describing the effects of labels on features, possibly accounting for…
Trupti Mohanty, K.S. Ravi Chandran, Taylor D. Sparks
Nickel and Cobalt based superalloys are commonly used as turbine materials for high-temperature applications. However, their maximum operating temperature is limited to about 1100oC. Therefore, to improve turbine efficiency, current research is focused on designing materials that can withstand higher temperatures.…
Rahi Jain, Wei Xu
Feature selection (FS) is critical for high dimensional data analysis. Ensemble based feature selection (EFS) is a commonly used approach to develop FS techniques. Rank aggregation (RA) is an essential step of EFS where results from multiple models are pooled to estimate feature importance. However, the literature…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Nazila Ahmadi Daryakenari, Seyed Kamaleddin Setaredan
Schizophrenia (SZ) is a chronic and complex mental disorder associated with neurobiological deficits. The complexity and heterogeneity of schizophrenia symptoms pose challenges for objective diagnosis, which is currently based on behavioral and clinical manifestations. Furthermore, other psychiatric disorders such as…
Noa L. Hedrich, Eric Schulz, Sam Hall-McMaster, Nicolas W. Schuck
Identifying goal-relevant features in novel environments is a central challenge for efficient behaviour. We asked whether humans address this challenge by relying on prior knowledge about common properties of reward-predicting features. One such property is the rate of change of features, given that behaviourally…