Header logo is


2019


Thumb xl fire
Controlling Heterogeneous Stochastic Growth Processes on Lattices with Limited Resources

Haksar, R., Solowjow, F., Trimpe, S., Schwager, M.

In Proceedings of the 58th IEEE International Conference on Decision and Control (CDC) , 58th IEEE International Conference on Decision and Control (CDC), December 2019 (proceedings) Accepted

ics

PDF [BibTex]

2019


PDF [BibTex]


no image
Prototyping Micro- and Nano-Optics with Focused Ion Beam Lithography

Keskinbora, K.

SL48, pages: 46, SPIE.Spotlight, SPIE Press, Bellingham, WA, 2019 (book)

mms

DOI [BibTex]

DOI [BibTex]

2015


Thumb xl thumb
Proceedings of the 37th German Conference on Pattern Recognition

Gall, J., Gehler, P., Leibe, B.

Springer, German Conference on Pattern Recognition, October 2015 (proceedings)

ps

GCPR conference website [BibTex]

2015


GCPR conference website [BibTex]


no image
Learning robots

Trimpe, S.

2015 (mpi_year_book)

Abstract
An exploded power plant, collapsed buildings after an earthquake, a burning vehicle loaded with hazardous goods – all of these are dangerous situations for human emergency responders. What if we could send robots instead of humans? Researchers at the Autonomous Motion Department work on fundamental principles required to build intelligent robots which one day can help us in dangerous situations. A key requirement for making this happen is that robots must be enabled to learn.

link (url) [BibTex]


no image
The smallest human-made nano-motor

Sánchez, Samuel

2015 (mpi_year_book)

Abstract
Tiny self-propelled motors which speed through the water and clean up pollutions along the way or small robots which can swim effortlessly through blood to one day transport medication to a certain part of the body – this sounds like taken from a science fiction movie script. However, Samuel Sánchez is already hard at work in his lab at the Max Planck Institute for Intelligent Systems in Stuttgart to make these visions come true. Self-propelled micro-nanorobots and the usage as integrated sensors in microfluid-chips: that’s the topic of Sánchez` research group.

link (url) [BibTex]

link (url) [BibTex]

2014


Thumb xl thumb 9780262028370
Advanced Structured Prediction

Nowozin, S., Gehler, P. V., Jancsary, J., Lampert, C. H.

Advanced Structured Prediction, pages: 432, Neural Information Processing Series, MIT Press, November 2014 (book)

Abstract
The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning.

ps

publisher link (url) [BibTex]

2014


publisher link (url) [BibTex]


no image
Learning Motor Skills: From Algorithms to Robot Experiments

Kober, J., Peters, J.

97, pages: 191, Springer Tracts in Advanced Robotics, Springer, 2014 (book)

ei

DOI [BibTex]

DOI [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]

2013


no image
Proceedings of the 10th European Workshop on Reinforcement Learning, Volume 24

Deisenroth, M., Szepesvári, C., Peters, J.

pages: 173, JMLR, European Workshop On Reinforcement Learning, EWRL, 2013 (proceedings)

ei

Web [BibTex]

2013


Web [BibTex]


no image
Perceiving Systems – Computers that see

Gehler, P. V.

2013 (mpi_year_book)

Abstract
Our research goal is to define in a mathematical precise way how visual perception works. We want to describe how intelligent systems understand images. To this end we study probabilistic models and statistical learning. Encoding prior knowledge about the world is complemented with automatic learning from training data. One aspect is being able to identify physical factors in images, such as lighting, geometry, and materials. Furthermore we want to automatically recognize and give names to objects and persons in images and understand the scene as a whole.

link (url) [BibTex]


no image
Being small, being smart

Liu, Na

2013 (mpi_year_book)

Abstract
Metallic nanostructures feature plasmonic resonances which spatially confine light on the nanometer scale. In the ultimate limit of a single nanostructure, the electromagnetic field can be strongly concentrated in a volume of only a few hundred nm3 or less. We utilize such plasmonic focusing for hydrogen detection at the single particle level, which avoids any inhomogeneous broadening and statistical effects that would occur in sensors based on nanoparticle ensembles. This concept paves the road towards the observation of single catalytic processes in nanoreactors.

link (url) [BibTex]

link (url) [BibTex]