15,004 research outputs found
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs
Stochastic differential equations are an important modeling class in many
disciplines. Consequently, there exist many methods relying on various
discretization and numerical integration schemes. In this paper, we propose a
novel, probabilistic model for estimating the drift and diffusion given noisy
observations of the underlying stochastic system. Using state-of-the-art
adversarial and moment matching inference techniques, we avoid the
discretization schemes of classical approaches. This leads to significant
improvements in parameter accuracy and robustness given random initial guesses.
On four established benchmark systems, we compare the performance of our
algorithms to state-of-the-art solutions based on extended Kalman filtering and
Gaussian processes.Comment: Published at the Thirty-sixth International Conference on Machine
Learning (ICML 2019
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing
Labeled data is a critical resource for training and evaluating machine
learning models. However, many real-life datasets are only partially labeled.
We propose a semi-supervised machine learning training strategy to improve
event detection performance on sequential data, such as video recordings, when
only sparse labels are available, such as event start times without their
corresponding end times. Our method uses noisy guesses of the events' end times
to train event detection models. Depending on how conservative these guesses
are, mislabeled false positives may be introduced into the training set (i.e.,
negative sequences mislabeled as positives). We further propose a mathematical
model for estimating how many inaccurate labels a model is exposed to, based on
how noisy the end time guesses are. Finally, we show that neural networks can
improve their detection performance by leveraging more training data with less
conservative approximations despite the higher proportion of incorrect labels.
We adapt sequential versions of MNIST and CIFAR-10 to empirically evaluate our
method, and find that our risk-tolerant strategy outperforms conservative
estimates by 12 points of mean average precision for MNIST, and 3.5 points for
CIFAR. Then, we leverage the proposed training strategy to tackle a real-life
application: processing continuous video recordings of epilepsy patients to
improve seizure detection, and show that our method outperforms baseline
labeling methods by 10 points of average precision
Basin structure of optimization based state and parameter estimation
Most data based state and parameter estimation methods require suitable
initial values or guesses to achieve convergence to the desired solution, which
typically is a global minimum of some cost function. Unfortunately, however,
other stable solutions (e.g., local minima) may exist and provide suboptimal or
even wrong estimates. Here we demonstrate for a 9-dimensional Lorenz-96 model
how to characterize the basin size of the global minimum when applying some
particular optimization based estimation algorithm. We compare three different
strategies for generating suitable initial guesses and we investigate the
dependence of the solution on the given trajectory segment (underlying the
measured time series). To address the question of how many state variables have
to be measured for optimal performance, different types of multivariate time
series are considered consisting of 1, 2, or 3 variables. Based on these time
series the local observability of state variables and parameters of the
Lorenz-96 model is investigated and confirmed using delay coordinates. This
result is in good agreement with the observation that correct state and
parameter estimation results are obtained if the optimization algorithm is
initialized with initial guesses close to the true solution. In contrast,
initialization with other exact solutions of the model equations (different
from the true solution used to generate the time series) typically fails, i.e.
the optimization procedure ends up in local minima different from the true
solution. Initialization using random values in a box around the attractor
exhibits success rates depending on the number of observables and the available
time series (trajectory segment).Comment: 15 pages, 2 figure
Inferring rate coefficents of biochemical reactions from noisy data with KInfer
Dynamical models of inter- and intra-cellular processes contain the rate constants of the biochemical reactions. These kinetic parameters are often not accessible directly through experiments, but they can be inferred from time-resolved data. Time resolved data, that is, measurements of reactant concentration at series of time points, are usually affected by different types of error, whose source can be both experimental and biological. The noise in the input data makes the estimation of the model parameters a very difficult task, as if the inference method is not sufficiently robust to the noise, the resulting estimates are not reliable. Therefore "noise-robust" methods that estimate rate constants with the maximum precision and accuracy are needed. In this report we present the probabilistic generative model of parameter inference implemented by the software prototype KInfer and we show the ability of this tool of estimating the rate coefficients of models of biochemical network with a good accuracy even from very noisy input data
On an adaptive regularization for ill-posed nonlinear systems and its trust-region implementation
In this paper we address the stable numerical solution of nonlinear ill-posed
systems by a trust-region method. We show that an appropriate choice of the
trust-region radius gives rise to a procedure that has the potential to
approach a solution of the unperturbed system. This regularizing property is
shown theoretically and validated numerically.Comment: arXiv admin note: text overlap with arXiv:1410.278
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