Header logo is


2014


no image
Single-Source Domain Adaptation with Target and Conditional Shift

Zhang, K., Schölkopf, B., Muandet, K., Wang, Z., Zhou, Z., Persello, C.

In Regularization, Optimization, Kernels, and Support Vector Machines, pages: 427-456, 19, Chapman & Hall/CRC Machine Learning & Pattern Recognition, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), Chapman and Hall/CRC, Boca Raton, USA, 2014 (inbook)

ei

[BibTex]

2014


[BibTex]


no image
Higher-Order Tensors in Diffusion Imaging

Schultz, T., Fuster, A., Ghosh, A., Deriche, R., Florack, L., Lim, L.

In Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data, pages: 129-161, Mathematics + Visualization, (Editors: Westin, C.-F., Vilanova, A. and Burgeth, B.), Springer, 2014 (inbook)

ei

[BibTex]

[BibTex]


no image
Fuzzy Fibers: Uncertainty in dMRI Tractography

Schultz, T., Vilanova, A., Brecheisen, R., Kindlmann, G.

In Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, pages: 79-92, 8, Mathematics + Visualization, (Editors: Hansen, C. D., Chen, M., Johnson, C. R., Kaufman, A. E. and Hagen, H.), Springer, 2014 (inbook)

ei

[BibTex]

[BibTex]


no image
Nonconvex Proximal Splitting with Computational Errors

Sra, S.

In Regularization, Optimization, Kernels, and Support Vector Machines, pages: 83-102, 4, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), CRC Press, 2014 (inbook)

ei

[BibTex]

[BibTex]


no image
Exploring complex diseases with intelligent systems

Borgwardt, K.

2014 (mpi_year_book)

Abstract
Physicians are collecting an ever increasing amount of data describing the health state of their patients. Is new knowledge about diseases hidden in this data, which could lead to better therapies? The field of Machine Learning in Biomedicine is concerned with the development of approaches which help to gain such insights from massive biomedical data.

link (url) [BibTex]


no image
The cellular life-death decision – how mitochondrial membrane proteins can determine cell fate

García-Sáez, Ana J.

2014 (mpi_year_book)

Abstract
Living organisms have a very effective method for eliminating cells that are no longer needed: programmed death. Researchers in the group of Ana García Sáez work with a protein called Bax, a key regulator of apoptosis that creates pores with a flexible diameter inside the outer mitochondrial membrane. This step inevitably triggers the final death of the cell. These insights into the role of important key enzymes in setting off apoptosis could provide useful for developing drugs that can directly influence apoptosis.

link (url) [BibTex]


no image
Addressing of Micro-robot Teams and Non-contact Micro-manipulation

Diller, E., Ye, Z., Giltinan, J., Sitti, M.

In Small-Scale Robotics. From Nano-to-Millimeter-Sized Robotic Systems and Applications, pages: 28-38, Springer Berlin Heidelberg, 2014 (incollection)

pi

Project Page [BibTex]

Project Page [BibTex]


no image
Robot Learning by Guided Self-Organization

Martius, G., Der, R., Herrmann, J. M.

In Guided Self-Organization: Inception, 9, pages: 223-260, Emergence, Complexity and Computation, Springer Berlin Heidelberg, 2014 (incollection)

al

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Thumb xl simulated annealing
Simulated Annealing

Gall, J.

In Encyclopedia of Computer Vision, pages: 737-741, 0, (Editors: Ikeuchi, K. ), Springer Verlag, 2014, to appear (inbook)

ps

[BibTex]

[BibTex]

1996


no image
From isolation to cooperation: An alternative of a system of experts

Schaal, S., Atkeson, C. G.

In Advances in Neural Information Processing Systems 8, pages: 605-611, (Editors: Touretzky, D. S.;Mozer, M. C.;Hasselmo, M. E.), MIT Press, Cambridge, MA, 1996, clmc (inbook)

Abstract
We introduce a constructive, incremental learning system for regression problems that models data by means of locally linear experts. In contrast to other approaches, the experts are trained independently and do not compete for data during learning. Only when a prediction for a query is required do the experts cooperate by blending their individual predictions. Each expert is trained by minimizing a penalized local cross validation error using second order methods. In this way, an expert is able to adjust the size and shape of the receptive field in which its predictions are valid, and also to adjust its bias on the importance of individual input dimensions. The size and shape adjustment corresponds to finding a local distance metric, while the bias adjustment accomplishes local dimensionality reduction. We derive asymptotic results for our method. In a variety of simulations we demonstrate the properties of the algorithm with respect to interference, learning speed, prediction accuracy, feature detection, and task oriented incremental learning. 

am

link (url) [BibTex]

1996


link (url) [BibTex]