37,875 research outputs found
Deep Directional Statistics: Pose Estimation with Uncertainty Quantification
Modern deep learning systems successfully solve many perception tasks such as
object pose estimation when the input image is of high quality. However, in
challenging imaging conditions such as on low-resolution images or when the
image is corrupted by imaging artifacts, current systems degrade considerably
in accuracy. While a loss in performance is unavoidable, we would like our
models to quantify their uncertainty in order to achieve robustness against
images of varying quality. Probabilistic deep learning models combine the
expressive power of deep learning with uncertainty quantification. In this
paper, we propose a novel probabilistic deep learning model for the task of
angular regression. Our model uses von Mises distributions to predict a
distribution over object pose angle. Whereas a single von Mises distribution is
making strong assumptions about the shape of the distribution, we extend the
basic model to predict a mixture of von Mises distributions. We show how to
learn a mixture model using a finite and infinite number of mixture components.
Our model allows for likelihood-based training and efficient inference at test
time. We demonstrate on a number of challenging pose estimation datasets that
our model produces calibrated probability predictions and competitive or
superior point estimates compared to the current state-of-the-art
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
Uncertainty quantification (UQ) is an important component of molecular
property prediction, particularly for drug discovery applications where model
predictions direct experimental design and where unanticipated imprecision
wastes valuable time and resources. The need for UQ is especially acute for
neural models, which are becoming increasingly standard yet are challenging to
interpret. While several approaches to UQ have been proposed in the literature,
there is no clear consensus on the comparative performance of these models. In
this paper, we study this question in the context of regression tasks. We
systematically evaluate several methods on five benchmark datasets using
multiple complementary performance metrics. Our experiments show that none of
the methods we tested is unequivocally superior to all others, and none
produces a particularly reliable ranking of errors across multiple datasets.
While we believe these results show that existing UQ methods are not sufficient
for all common use-cases and demonstrate the benefits of further research, we
conclude with a practical recommendation as to which existing techniques seem
to perform well relative to others
Doubly Optimized Calibrated Support Vector Machine (DOC-SVM): an algorithm for joint optimization of discrimination and calibration.
Historically, probabilistic models for decision support have focused on discrimination, e.g., minimizing the ranking error of predicted outcomes. Unfortunately, these models ignore another important aspect, calibration, which indicates the magnitude of correctness of model predictions. Using discrimination and calibration simultaneously can be helpful for many clinical decisions. We investigated tradeoffs between these goals, and developed a unified maximum-margin method to handle them jointly. Our approach called, Doubly Optimized Calibrated Support Vector Machine (DOC-SVM), concurrently optimizes two loss functions: the ridge regression loss and the hinge loss. Experiments using three breast cancer gene-expression datasets (i.e., GSE2034, GSE2990, and Chanrion's datasets) showed that our model generated more calibrated outputs when compared to other state-of-the-art models like Support Vector Machine (p=0.03, p=0.13, and p<0.001) and Logistic Regression (p=0.006, p=0.008, and p<0.001). DOC-SVM also demonstrated better discrimination (i.e., higher AUCs) when compared to Support Vector Machine (p=0.38, p=0.29, and p=0.047) and Logistic Regression (p=0.38, p=0.04, and p<0.0001). DOC-SVM produced a model that was better calibrated without sacrificing discrimination, and hence may be helpful in clinical decision making
The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
The numerous recent breakthroughs in machine learning (ML) make imperative to
carefully ponder how the scientific community can benefit from a technology
that, although not necessarily new, is today living its golden age. This Grand
Challenge review paper is focused on the present and future role of machine
learning in space weather. The purpose is twofold. On one hand, we will discuss
previous works that use ML for space weather forecasting, focusing in
particular on the few areas that have seen most activity: the forecasting of
geomagnetic indices, of relativistic electrons at geosynchronous orbits, of
solar flares occurrence, of coronal mass ejection propagation time, and of
solar wind speed. On the other hand, this paper serves as a gentle introduction
to the field of machine learning tailored to the space weather community and as
a pointer to a number of open challenges that we believe the community should
undertake in the next decade. The recurring themes throughout the review are
the need to shift our forecasting paradigm to a probabilistic approach focused
on the reliable assessment of uncertainties, and the combination of
physics-based and machine learning approaches, known as gray-box.Comment: under revie
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