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While several methods for predicting uncertainty on deep networks have been recently proposed, they do not always readily translate to large and complex datasets without significant overhead. In this paper we utilize a special instance of the Mixture Density Networks (MDNs) to produce an elegant and compact approach to quantity uncertainty in regression problems. When applied to standard regression benchmark datasets, we show an improvement in predictive log-likelihood and root-mean-square-error when compared to existing state-of-the-art methods. We demonstrate the efficacy and practical usefulness of the method for (i) predicting future stock prices from stochastic, highly volatile time-series data; (ii) anomaly detection in real-life highly complex video segments; and (iii) the task of age estimation and data cleansing on the challenging IMDb-Wiki dataset of half a million face images.
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This content will become publicly available on November 13, 2025
Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function
Microbial communities play key roles across diverse environments. Predicting their function and dynamics is a key goal of microbial ecology, but detailed microscopic descriptions of these systems can be prohibitively complex. One approach to deal with this complexity is to resort to coarser representations. Several approaches have sought to identify useful groupings of microbial species in a data-driven way. Of these, recent work has claimed some empirical success at de novo discovery of coarse representations predictive of a given function using methods as simple as a linear regression, against multiple groups of species or even a single such group (the ensemble quotient optimization (EQO) approach). Modeling community function as a linear combination of individual species’ contributions appears simplistic. However, the task of identifying a predictive coarsening of an ecosystem is distinct from the task of predicting the function well, and it is conceivable that the former could be accomplished by a simpler methodology than the latter. Here, we use the resource competition framework to design a model where the “correct” grouping to be discovered is well-defined, and use synthetic data to evaluate and compare three regression-based methods, namely, two proposed previously and one we introduce. We find that regression-based methods can recover the groupings even when the function is manifestly nonlinear; that multi-group methods offer an advantage over a single-group EQO; and crucially, that simpler (linear) methods can outperform more complex ones.
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- PAR ID:
- 10581085
- Editor(s):
- Oña, Leonardo
- Publisher / Repository:
- Public Library of Science
- Date Published:
- Journal Name:
- PLOS Computational Biology
- Volume:
- 20
- Issue:
- 11
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1012590
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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