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2015


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Exploiting Object Similarity in 3D Reconstruction

Zhou, C., Güney, F., Wang, Y., Geiger, A.

In International Conference on Computer Vision (ICCV), December 2015 (inproceedings)

Abstract
Despite recent progress, reconstructing outdoor scenes in 3D from movable platforms remains a highly difficult endeavor. Challenges include low frame rates, occlusions, large distortions and difficult lighting conditions. In this paper, we leverage the fact that the larger the reconstructed area, the more likely objects of similar type and shape will occur in the scene. This is particularly true for outdoor scenes where buildings and vehicles often suffer from missing texture or reflections, but share similarity in 3D shape. We take advantage of this shape similarity by locating objects using detectors and jointly reconstructing them while learning a volumetric model of their shape. This allows us to reduce noise while completing missing surfaces as objects of similar shape benefit from all observations for the respective category. We evaluate our approach with respect to LIDAR ground truth on a novel challenging suburban dataset and show its advantages over the state-of-the-art.

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pdf suppmat [BibTex]

2015


pdf suppmat [BibTex]


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FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation

Lenz, P., Geiger, A., Urtasun, R.

In International Conference on Computer Vision (ICCV), International Conference on Computer Vision (ICCV), December 2015 (inproceedings)

Abstract
One of the most popular approaches to multi-target tracking is tracking-by-detection. Current min-cost flow algorithms which solve the data association problem optimally have three main drawbacks: they are computationally expensive, they assume that the whole video is given as a batch, and they scale badly in memory and computation with the length of the video sequence. In this paper, we address each of these issues, resulting in a computationally and memory-bounded solution. First, we introduce a dynamic version of the successive shortest-path algorithm which solves the data association problem optimally while reusing computation, resulting in faster inference than standard solvers. Second, we address the optimal solution to the data association problem when dealing with an incoming stream of data (i.e., online setting). Finally, we present our main contribution which is an approximate online solution with bounded memory and computation which is capable of handling videos of arbitrary length while performing tracking in real time. We demonstrate the effectiveness of our algorithms on the KITTI and PETS2009 benchmarks and show state-of-the-art performance, while being significantly faster than existing solvers.

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pdf suppmat video project [BibTex]

pdf suppmat video project [BibTex]


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Enzymatically active biomimetic micropropellers for the penetration of mucin gels

Walker (Schamel), D., Käsdorf, B. T., Jeong, H. H., Lieleg, O., Fischer, P.

Science Advances, 1(11):e1500501, December 2015 (article)

Abstract
In the body, mucus provides an important defense mechanism by limiting the penetration of pathogens. It is therefore also a major obstacle for the efficient delivery of particle-based drug carriers. The acidic stomach lining in particular is difficult to overcome because mucin glycoproteins form viscoelastic gels under acidic conditions. The bacterium Helicobacter pylori has developed a strategy to overcome the mucus barrier by producing the enzyme urease, which locally raises the pH and consequently liquefies the mucus. This allows the bacteria to swim through mucus and to reach the epithelial surface. We present an artificial system of reactive magnetic micropropellers that mimic this strategy to move through gastric mucin gels by making use of surface-immobilized urease. The results demonstrate the validity of this biomimetic approach to penetrate biological gels, and show that externally propelled microstructures can actively and reversibly manipulate the physical state of their surroundings, suggesting that such particles could potentially penetrate native mucus.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results

Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S.

Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS), pages: , , Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (conference)

Abstract
This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Preliminary results of a low-dimensional tuning problem highlight the method’s potential for automatic controller tuning on robotic platforms.

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PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


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Towards Probabilistic Volumetric Reconstruction using Ray Potentials

(Best Paper Award)

Ulusoy, A. O., Geiger, A., Black, M. J.

In 3D Vision (3DV), 2015 3rd International Conference on, pages: 10-18, Lyon, October 2015 (inproceedings)

Abstract
This paper presents a novel probabilistic foundation for volumetric 3-d reconstruction. We formulate the problem as inference in a Markov random field, which accurately captures the dependencies between the occupancy and appearance of each voxel, given all input images. Our main contribution is an approximate highly parallelized discrete-continuous inference algorithm to compute the marginal distributions of each voxel's occupancy and appearance. In contrast to the MAP solution, marginals encode the underlying uncertainty and ambiguity in the reconstruction. Moreover, the proposed algorithm allows for a Bayes optimal prediction with respect to a natural reconstruction loss. We compare our method to two state-of-the-art volumetric reconstruction algorithms on three challenging aerial datasets with LIDAR ground truth. Our experiments demonstrate that the proposed algorithm compares favorably in terms of reconstruction accuracy and the ability to expose reconstruction uncertainty.

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code YouTube pdf suppmat DOI Project Page [BibTex]

code YouTube pdf suppmat DOI Project Page [BibTex]


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The EChemPen: A Guiding Hand To Learn Electrochemical Surface Modifications

Valetaud, M., Loget, G., Roche, J., Hueken, N., Fattah, Z., Badets, V., Fontaine, O., Zigah, D.

J. of Chem. Ed., 92(10):1700-1704, September 2015 (article)

Abstract
The Electrochemical Pen (EChemPen) was developed as an attractive tool for learning electrochemistry. The fabrication, principle, and operation of the EChemPen are simple and can be easily performed by students in practical classes. It is based on a regular fountain pen principle, where the electrolytic solution is dispensed at a tip to locally modify a conductive surface by triggering a localized electrochemical reaction. Three simple model reactions were chosen to demonstrate the versatility of the EChemPen for teaching various electrochemical processes. We describe first the reversible writing/erasing of metal letters, then the electrodeposition of a black conducting polymer "ink", and finally the colorful writings that can be generated by titanium anodization and that can be controlled by the applied potential. These entertaining and didactic experiments are adapted for teaching undergraduate students that start to study electrochemistry by means of surface modification reactions.

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DOI [BibTex]

DOI [BibTex]


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3D-printed Soft Microrobot for Swimming in Biological Fluids

Qiu, T., Palagi, S., Fischer, P.

In Conf. Proc. IEEE Eng. Med. Biol. Soc., pages: 4922-4925, August 2015 (inproceedings)

Abstract
Microscopic artificial swimmers hold the potential to enable novel non-invasive medical procedures. In order to ease their translation towards real biomedical applications, simpler designs as well as cheaper yet more reliable materials and fabrication processes should be adopted, provided that the functionality of the microrobots can be kept. A simple single-hinge design could already enable microswimming in non-Newtonian fluids, which most bodily fluids are. Here, we address the fabrication of such single-hinge microrobots with a 3D-printed soft material. Firstly, a finite element model is developed to investigate the deformability of the 3D-printed microstructure under typical values of the actuating magnetic fields. Then the microstructures are fabricated by direct 3D-printing of a soft material and their swimming performances are evaluated. The speeds achieved with the 3D-printed microrobots are comparable to those obtained in previous work with complex fabrication procedures, thus showing great promise for 3D-printed microrobots to be operated in biological fluids.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Displets: Resolving Stereo Ambiguities using Object Knowledge

Güney, F., Geiger, A.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015, pages: 4165-4175, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2015 (inproceedings)

Abstract
Stereo techniques have witnessed tremendous progress over the last decades, yet some aspects of the problem still remain challenging today. Striking examples are reflecting and textureless surfaces which cannot easily be recovered using traditional local regularizers. In this paper, we therefore propose to regularize over larger distances using object-category specific disparity proposals (displets) which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image. The proposed displets encode the fact that objects of certain categories are not arbitrarily shaped but typically exhibit regular structures. We integrate them as non-local regularizer for the challenging object class 'car' into a superpixel based CRF framework and demonstrate its benefits on the KITTI stereo evaluation.

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pdf abstract suppmat [BibTex]

pdf abstract suppmat [BibTex]


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Object Scene Flow for Autonomous Vehicles

Menze, M., Geiger, A.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015, pages: 3061-3070, IEEE, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2015 (inproceedings)

Abstract
This paper proposes a novel model and dataset for 3D scene flow estimation with an application to autonomous driving. Taking advantage of the fact that outdoor scenes often decompose into a small number of independently moving objects, we represent each element in the scene by its rigid motion parameters and each superpixel by a 3D plane as well as an index to the corresponding object. This minimal representation increases robustness and leads to a discrete-continuous CRF where the data term decomposes into pairwise potentials between superpixels and objects. Moreover, our model intrinsically segments the scene into its constituting dynamic components. We demonstrate the performance of our model on existing benchmarks as well as a novel realistic dataset with scene flow ground truth. We obtain this dataset by annotating 400 dynamic scenes from the KITTI raw data collection using detailed 3D CAD models for all vehicles in motion. Our experiments also reveal novel challenges which can't be handled by existing methods.

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pdf abstract suppmat DOI [BibTex]

pdf abstract suppmat DOI [BibTex]


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Optimal Length of Low Reynolds Number Nanopropellers

Walker (Schamel), D., Kuebler, M., Morozov, K. I., Fischer, P., Leshansky, A. M.

Nano Letters, 15(7):4412-4416, June 2015 (article)

Abstract
Locomotion in fluids at the nanoscale is dominated by viscous drag. One efficient propulsion scheme is to use a weak rotating magnetic field that drives a chiral object. Froth bacterial flagella to artificial drills, the corkscrew is a universally useful chiral shape for propulsion in viscous environments. Externally powered magnetic micro- and nanomotors have been recently developed that allow for precise fuel-free propulsion in complex media. Here, we combine analytical and numerical theory with experiments on nanostructured screw-propellers to show that the optimal length is surprisingly short only about one helical turn, which is shorter than most of the structures in use to date. The results have important implications for the design of artificial actuated nano- and micropropellers and can dramatically reduce fabrication times, while ensuring optimal performance.

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DOI [BibTex]

DOI [BibTex]


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A theoretical study of potentially observable chirality-sensitive NMR effects in molecules

Garbacz, P., Cukras, J., Jaszunski, M.

Phys. Chem. Chem. Phys., 17(35):22642-22651, May 2015 (article)

Abstract
Two recently predicted nuclear magnetic resonance effects, the chirality-induced rotating electric polarization and the oscillating magnetization, are examined for several experimentally available chiral molecules. We discuss in detail the requirements for experimental detection of chirality-sensitive NMR effects of the studied molecules. These requirements are related to two parameters: the shielding polarizability and the antisymmetric part of the nuclear magnetic shielding tensor. The dominant second contribution has been computed for small molecules at the coupled cluster and density functional theory levels. It was found that DFT calculations using the KT2 functional and the aug-cc-pCVTZ basis set adequately reproduce the CCSD(T) values obtained with the same basis set. The largest values of parameters, thus most promising from the experimental point of view, were obtained for the fluorine nuclei in 1,3-difluorocyclopropene and 1,3-diphenyl-2-fluoro-3-trifluoromethylcyclopropene.

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DOI [BibTex]

DOI [BibTex]


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Dynamic Inclusion Complexes of Metal Nanoparticles Inside Nanocups

Alarcon-Correa, M., Lee, T. C., Fischer, P.

Angew. Chem. Int. Ed., 54(23):6730-6734, May 2015, Featured cover article. (article)

Abstract
Host-guest inclusion complexes are abundant in molecular systems and of fundamental importance in living organisms. Realizing a colloidal analogue of a molecular dynamic inclusion complex is challenging because inorganic nanoparticles (NPs) with a well-defined cavity and portal are difficult to synthesize in high yield and with good structural fidelity. Herein, a generic strategy towards the fabrication of dynamic 1: 1 inclusion complexes of metal nanoparticles inside oxide nanocups with high yield (> 70%) and regiospecificity (> 90%) by means of a reactive double Janus nanoparticle intermediate is reported. Experimental evidence confirms that the inclusion complexes are formed by a kinetically controlled mechanism involving a delicate interplay between bipolar galvanic corrosion and alloying-dealloying oxidation. Release of the NP guest from the nanocups can be efficiently triggered by an external stimulus. Featured cover article.

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DOI [BibTex]

DOI [BibTex]


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Surface roughness-induced speed increase for active Janus micromotors

Choudhury, U., Soler, L., Gibbs, J. G., Sanchez, S., Fischer, P.

Chem. Comm., 51(41):8660-8663, April 2015 (article)

Abstract
We demonstrate a simple physical fabrication method to control surface roughness of Janus micromotors and fabricate self-propelled active Janus microparticles with rough catalytic platinum surfaces that show a four-fold increase in their propulsion speed compared to conventional Janus particles coated with a smooth Pt layer.

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DOI [BibTex]

DOI [BibTex]


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Active colloidal microdrills

Gibbs, J. G., Fischer, P.

Chem. Comm., 51(20):4192-4195, Febuary 2015 (article)

Abstract
We demonstrate a chemically driven, autonomous catalytic microdrill. An asymmetric distribution of catalyst causes the helical swimmer to twist while it undergoes directed propulsion. A driving torque and hydrodynamic coupling between translation and rotation at low Reynolds number leads to drill-like swimming behaviour.

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DOI [BibTex]

DOI [BibTex]


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Inference of Cause and Effect with Unsupervised Inverse Regression

Sgouritsa, E., Janzing, D., Hennig, P., Schölkopf, B.

In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 38, pages: 847-855, JMLR Workshop and Conference Proceedings, (Editors: Lebanon, G. and Vishwanathan, S.V.N.), JMLR.org, AISTATS, 2015 (inproceedings)

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Web PDF [BibTex]

Web PDF [BibTex]


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Probabilistic Interpretation of Linear Solvers

Hennig, P.

SIAM Journal on Optimization, 25(1):234-260, 2015 (article)

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Web PDF link (url) DOI [BibTex]

Web PDF link (url) DOI [BibTex]


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Joint 3D Object and Layout Inference from a single RGB-D Image

(Best Paper Award)

Geiger, A., Wang, C.

In German Conference on Pattern Recognition (GCPR), 9358, pages: 183-195, Lecture Notes in Computer Science, Springer International Publishing, 2015 (inproceedings)

Abstract
Inferring 3D objects and the layout of indoor scenes from a single RGB-D image captured with a Kinect camera is a challenging task. Towards this goal, we propose a high-order graphical model and jointly reason about the layout, objects and superpixels in the image. In contrast to existing holistic approaches, our model leverages detailed 3D geometry using inverse graphics and explicitly enforces occlusion and visibility constraints for respecting scene properties and projective geometry. We cast the task as MAP inference in a factor graph and solve it efficiently using message passing. We evaluate our method with respect to several baselines on the challenging NYUv2 indoor dataset using 21 object categories. Our experiments demonstrate that the proposed method is able to infer scenes with a large degree of clutter and occlusions.

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pdf suppmat video project DOI [BibTex]

pdf suppmat video project DOI [BibTex]


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Probabilistic Line Searches for Stochastic Optimization

Mahsereci, M., Hennig, P.

In Advances in Neural Information Processing Systems 28, pages: 181-189, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (inproceedings)

Abstract
In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space. We construct a probabilistic line search by combining the structure of existing deterministic methods with notions from Bayesian optimization. Our method retains a Gaussian process surrogate of the univariate optimization objective, and uses a probabilistic belief over the Wolfe conditions to monitor the descent. The algorithm has very low computational cost, and no user-controlled parameters. Experiments show that it effectively removes the need to define a learning rate for stochastic gradient descent. [You can find the matlab research code under `attachments' below. The zip-file contains a minimal working example. The docstring in probLineSearch.m contains additional information. A more polished implementation in C++ will be published here at a later point. For comments and questions about the code please write to mmahsereci@tue.mpg.de.]

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Matlab research code link (url) [BibTex]

Matlab research code link (url) [BibTex]


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Selectable Nanopattern Arrays for Nanolithographic Imprint and Etch-Mask Applications

Jeong, H. H., Mark, A. G., Lee, T., Son, K., Chen, W., Alarcon-Correa, M., Kim, I., Schütz, G., Fischer, P.

Adv. Science, 2(7):1500016, 2015, Featured cover article. (article)

Abstract
A parallel nanolithographic patterning method is presented that can be used to obtain arrays of multifunctional nanoparticles. These patterns can simply be converted into a variety of secondary nanopatterns that are useful for nanolithographic imprint, plasmonic, and etch-mask applications.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Discrete Optimization for Optical Flow

Menze, M., Heipke, C., Geiger, A.

In German Conference on Pattern Recognition (GCPR), 9358, pages: 16-28, Springer International Publishing, 2015 (inproceedings)

Abstract
We propose to look at large-displacement optical flow from a discrete point of view. Motivated by the observation that sub-pixel accuracy is easily obtained given pixel-accurate optical flow, we conjecture that computing the integral part is the hardest piece of the problem. Consequently, we formulate optical flow estimation as a discrete inference problem in a conditional random field, followed by sub-pixel refinement. Naive discretization of the 2D flow space, however, is intractable due to the resulting size of the label set. In this paper, we therefore investigate three different strategies, each able to reduce computation and memory demands by several orders of magnitude. Their combination allows us to estimate large-displacement optical flow both accurately and efficiently and demonstrates the potential of discrete optimization for optical flow. We obtain state-of-the-art performance on MPI Sintel and KITTI.

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pdf suppmat project DOI [BibTex]

pdf suppmat project DOI [BibTex]


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A Random Riemannian Metric for Probabilistic Shortest-Path Tractography

Hauberg, S., Schober, M., Liptrot, M., Hennig, P., Feragen, A.

In 18th International Conference on Medical Image Computing and Computer Assisted Intervention, 9349, pages: 597-604, Lecture Notes in Computer Science, MICCAI, 2015 (inproceedings)

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PDF DOI [BibTex]

PDF DOI [BibTex]


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Joint 3D Estimation of Vehicles and Scene Flow

Menze, M., Heipke, C., Geiger, A.

In Proc. of the ISPRS Workshop on Image Sequence Analysis (ISA), 2015 (inproceedings)

Abstract
Three-dimensional reconstruction of dynamic scenes is an important prerequisite for applications like mobile robotics or autonomous driving. While much progress has been made in recent years, imaging conditions in natural outdoor environments are still very challenging for current reconstruction and recognition methods. In this paper, we propose a novel unified approach which reasons jointly about 3D scene flow as well as the pose, shape and motion of vehicles in the scene. Towards this goal, we incorporate a deformable CAD model into a slanted-plane conditional random field for scene flow estimation and enforce shape consistency between the rendered 3D models and the parameters of all superpixels in the image. The association of superpixels to objects is established by an index variable which implicitly enables model selection. We evaluate our approach on the challenging KITTI scene flow dataset in terms of object and scene flow estimation. Our results provide a prove of concept and demonstrate the usefulness of our method.

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PDF [BibTex]

PDF [BibTex]


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Probabilistic numerics and uncertainty in computations

Hennig, P., Osborne, M. A., Girolami, M.

Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 471(2179), 2015 (article)

Abstract
We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation with limited time or hardware, are important for much contemporary science and industry. Within applications such as climate science and astrophysics, the need to make decisions on the basis of computations with large and complex data have led to a renewed focus on the management of numerical uncertainty. We describe how several seminal classic numerical methods can be interpreted naturally as probabilistic inference. We then show that the probabilistic view suggests new algorithms that can flexibly be adapted to suit application specifics, while delivering improved empirical performance. We provide concrete illustrations of the benefits of probabilistic numeric algorithms on real scientific problems from astrometry and astronomical imaging, while highlighting open problems with these new algorithms. Finally, we describe how probabilistic numerical methods provide a coherent framework for identifying the uncertainty in calculations performed with a combination of numerical algorithms (e.g. both numerical optimizers and differential equation solvers), potentially allowing the diagnosis (and control) of error sources in computations.

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PDF DOI [BibTex]

PDF DOI [BibTex]