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Forschungsseminar

Forschungsseminar

Das Forschungsseminar ist eine Veranstaltung, die sich an interessierte Studenten des Hauptstudiums richtet (bzw. Master oder höheres Semester Bachelor). Andere Interessenten sind jedoch jederzeit herzlich willkommen! Die vortragenden Studenten und Mitarbeiter der Professur KI stellen aktuelle forschungsorientierte Themen vor. Vorträge werden in der Regel in Englisch gehalten. Das Seminar findet unregelmäßig im Raum 336 statt. Den genauen Termin einzelner Veranstaltungen entnehmen Sie bitte den Ankündigungen auf dieser Seite.

Informationen für Diplom- und Masterstudenten

Die im Studium enthaltenen Seminarvorträge (das "Hauptseminar" im Studiengang Diplom-IF/AIF bzw. das "Forschungsseminar" im Master) können ebenso im Rahmen dieser Veranstaltung durchgeführt werden. Beide Lehrveranstaltungen (Diplom-Hauptseminar und Master-Forschungsseminar) haben das Ziel, dass die Teilnehmer selbststängig forschungsrelevantes Wissen erarbeiten und es anschließend im Rahmen eines Vortrages präsentieren. Thematisch behandeln die Seminare das Gebiet der Künstlichen Intelligenz, wobei der Schwerpunkt auf Objekterkennung, Neurocomputing auf Grafikkarten und Multi-Core Rechnern, Reinforcement Lernen, sowie intelligente Agenten in Virtueller Realität liegt. Andere Themenvorschläge sind aber ebenso herzlich willkommen!
Das Seminar wird nach individueller Absprache durchgeführt. Interessierte Studenten können unverbindlich Prof. Hamker kontaktieren, wenn sie wenn sie ein Interesse haben, bei uns eine der beiden Seminarveranstaltungen abzulegen.

Vergangene Veranstaltungen

Classification of Aircraft Cabin Configuration Packages using Machine Learning

Sreenath Ottapurakkal Kallada

Fri, 18. 8. 2017, Room 1/336

Aircraft manufacturers are constantly trying to enhance the comfort of the passengers in the cabin. But the tradeoff with increasing the comfort adversely affects the maximum number of passengers an aircraft can carry. This will gradually affect the overall profit for the airlines. Designing a good aircraft cabin requires careful analysis of the various monuments which are placed inside the cabin and their corresponding features. These monuments can range from the crew rest compartment to the galleys where the food is stored. Each of these monuments has exclusive features which can be used as a guiding metric to revise the cabin architecture. These revisions of cabin layouts may occur frequently during the development process where many marketing layouts are pitched to the prospective customers but not all of them make it to the final production line. The A350 program from Airbus is analyzed in this master thesis where the cabin layout data is retrieved from various XML sheets using Python parsers and merged together into a processible format. Various feature selection methods belonging to filter, wrapper and embedded methods are used to analyze the important features of the monuments and cabin layouts. Finally, three tree classifiers (single decision tree classifier, ensemble tree classifier, boosted tree classifier) are fitted on the datasets to classify the cabin layouts and monuments. The performance of the classifiers and the analysis of the relationship between the predictors and the target variable are recorded.

Automating Scientific Work in Optimization

Prof. Dr. Thomas Weise

Fri, 21. 7. 2017, Room 1/336

In the fields of heuristic optimization and machine learning, experimentation is the way to assess the performance of an algorithm setup and the hardness of problems. Good experimentation is complicated. Most algorithms in the domain are anytime algorithms, meaning they can improve their approximation quality over time. This means that one algorithm may initially perform better than another one but converge to worse solutions in the end. Instead of single final results, the whole runtime behavior of algorithms needs to be compared (and runtime may be measured in multiple ways). We do not just want to know which algorithm performs best and which problem is the hardest - a researcher wants to know why. In this paper, we introduce a methodology which can 1) automatically model the progress of algorithm setups on different problem instances based on data collected in experiments, 2) use these models to discover clusters of algorithm (and problem instance) behaviors, and 3) propose reasons why a certain algorithm setup (problem instance) belongs to a certain algorithm (instance) behavior cluster. These high-level conclusions are presented in form of decision trees relating algorithm parameters (instance features) to cluster ids. We emphasize the duality of analyzing algorithm setups and problem instances. Our methodology is implemented as open source software and applied in two case studies. Besides its basic application to raw experimental data, yielding clusters and explanations of quantitative algorithm behavior, our methodology also allows for qualitative conclusions by feeding it with data which is normalized with problem features or algorithm parameters. It can also be applied recursively, e.g., to further investigate the behavior of the algorithms in the cluster with the best-performing setups on the problem instances belonging to the cluster of hardest instances. Both use cases are investigated in the case studies.

Erweiterung der S2000 Sensorplattform für die Verwendung auf einer mobilen Plattform.

Shadi Saleh

Thu, 13. 7. 2017, Room 1/367

Stereo cameras are important sensors to provide good recognition of objects with color perception and very high resolution. It has been used in a wide range of automotive application prototypes, especially in advanced driver assistant system (ADAS). In this thesis, different methods based only on a stereo vision system are proposed, in order to estimate the ego-motion within a dynamic environment and extract information about the moving objects and stationary background. This is realized using Intenta S2000 sensor, a new generation of intelligent cameras that generates 3D point cloud at each time stamp. However, constructing the static objects map and dynamic objects list is a challenging task, since the motion is introduced by a combination between the movement of the camera and objects. The first proposed solution is achieved based on the dense optical flow, which illustrates the possibility of estimation ego-motion, and splitting the moving object from the background. In the second proposed solution, the ego-motion within a dynamic environment is realized using methods for solving a 3D-to-3D point registration problem. The iterative closest point (ICP) method is used to solve this problem by minimizing the geometric differences function between two consecutive 3D point clouds. Then the subtraction between the two aligned clouds is computed where the output result represents the subset of the 3D points from the objects in the environment. The most effective method for estimating the multiple moving and static objects is presented in the third proposed solution. The ego-motion is estimated based on the ICP method and then the post-processed 3D point cloud is projected on a 2D horizontal grid and based on the additional features to each grid cells is possible to distinguish between different grid cells into free, occluded and border. The cells status can be used later to construct the static object map and dynamic object list. Determination of noise regions is an essential step, which can be used later to generate 3D point cloud with low level of noise. This is done depending on the unsupervised segmentation methods.

Multi-GPU Simulation of Spiking Neural Networks

Joseph Gussev

Tue, 16. 5. 2017, Room 1/367

With the increasing importance of parallel programming in the context of achieving more computation power, general purpose computation on graphics processing units (GPGPU) has become more relevant for scientific computing. An example would be the simulation of spiking neural networks. During this talk, an introduction to GPU programming as well as the basics of spiking network simulation on GPUs is given. Afterwards, the concepts of Multi-GPU spiking network simulation used by the neural network simulators HRLSim and NCS will be explained.

Sound Source Detection and Localizing Active Person during Conversation

Ranjithkumar Jayaraman

Tue, 11. 4. 2017, Room 1/367

Robots has been emerging for interaction with people. Finding the correct sound source is very important for engaging in conversation between people or for being a co-worker with human. This project aims to find the sound direction and to get the source person of the sound in NAO robot. Sound direction can be determined easily with two microphones in free space, but when it placed in uneven spherical head of robot became complex because of diffraction and scattering of sound waves at the surfaces of robot head. Linear and logistic regression algorithm had been used to localize the direction of the sound source. OpenCV libraries had been used to find the human face in the sound direction. Finally we associate the sound source direction with the detected human faces. This algorithm promises to return robots more interactive while lessening to a conversation or working together with human co-workers.

Modelling attention in the neurons of area MT

Christine Ripken

Wed, 15. 3. 2017, Room 1/208

In order to model attention for motion detecting neurons, responses of the Model of Neuronal Responses in visual area MT (middle temporal area) of Simoncelli and Heeger were processed by the Mechanistic Cortical Microcircuit of Attention of Beuth and Hamker. The Mechanistic Cortical Microcircuit of Attention has replicated a great range of data sets, only implementing four basic neural mechanisms: amplification, spatial pooling, divisive normalization and suppression. The Model of Neuronal Responses in Visual Area MT is replicating very well recordings from area MT. The aim was to replicate responses of a representative MT neuron, whose data was collected by Maunsell and Lee. The defense will give a overview over the models and discuss briefly the results, which do not yet replicate the physiological data to a satisfying extent.

Untersuchung der Auswirkungen von Feedback in einem Modell des visuellen Systems

Daniel Johannes Lohmeier-von Laer

Wed, 15. 3. 2017, Room 1/208

Das visuelle System ist nicht nur auf Feedforward- und Laterale-Verarbeitung begrenzt, es gibt auch Rückverbindungen von Arealen höherer Hierarchie in frühere kortikale Areale. Welche Rolle diese Rückverbindungen bzw. Feedback spielt, ist noch nicht ausreichend verstanden und im Vergleich zur Feedforward-Hierarchie weniger erforscht. In einem Modell, dass den primären und sekundären visuellen Kortex umfasst, wurde eine Feedbackverbindung von V2 nach V1 hinzugefügt. Da die Wirkung des Feedbacks auf der Aktivität der V2-Neuronen beruht, wird diese Aktivität zusätzlich gesteuert werden, indem Aufmerksamkeit in der V2-Schicht simuliert wird. Wie stark der Feedbackstrom in Relation zu Feedforward ist, was er bewirkt und welche Auswirkungen die Simulation von Aufmerksamkeit auf die rezeptiven Felder der Neurone hat, wird in diesem Vortrag vorgestellt.

Fast simulation of neural networks using OpenMPI

Florian Polster

Tue, 14. 3. 2017, Room 1/208

In order to make the simulation of very large neural networks possible, ANNarchy has been extended by an MPI implementation. ANNarchy uses Cython to transfer the simulation setup from the Python application to the generated C++ simulator, which is unsuitable in a distributed context and therefore replaced by a combination of ZeroMQ (for transmission) and Protobuf (for serialization). For the distributed simulation a neural network is partitioned into subgraphs by splitting populations and projections by post-synaptic neurons.

Inhibition decorrelates neuronal activities in a network learning V1 simple-cells by voltage-based STDP and homeostatic inhibitory plasticity

Rene Larisch

Tue, 14. 3. 2017, Room 1/208

In the past years, many models to learn V1 simple-cells from natural scene input which use spike-timing dependent plasticity are published. However, these models have several limitations as they rely on theoretical approaches about the characteristics of the V1 simple-cells instead of biologically plausible neuronal mechanisms. We implemented a spiking neural network with excitatory and recurrently connected inhibitory neurons, receiving inputs from Poisson neurons. The excitatory synapses are learned with a phenomenologically voltage-based triplet STDP rule, from Clopath et al. (2010), and the inhibitory synapses are learned with a symmetric inhibitory STDP rule, from Vogels et al. (2011). To analyze the effect of inhibition during learning, we compare model implementations without inhibition and with different levels of inhibition. Furthermore, to study the impacts on the neuronal activity, we compare a model learned with inhibition to itself without inhibition. We see, that inhibition leads to better differentiated receptive fields, with decreased correlation between the neuron's activities and improved robustness to small input differences. Furthermore, we can reproduce observations from other computational V1 simple-cell models and from physiological experiments.

Deep Reinforcement Learning in robotics

Winfried Lötzsch

Mon, 27. 2. 2017, Room 1/367

For creating intelligently and autonomously operating robotics systems, Reinforcement Learning has longly been the method of choice. Combining the Reinforcement Learning principle with a deep neural network does not only improve the performance of these algorithms on commonly known robotics tasks, but also enables more complex tasks to be performed. I will show ways to implement this combination for both simulated and real robotics systems. One approach is to train the deep network to directly output motor commands for the robot [https://arxiv.org/pdf/1504.00702v5.pdf], but it can also be used to predict the success of a given action [https://arxiv.org/pdf/1603.02199v4.pdf]. Furthermore, recent work has shown, that using multiple learner instances in parallel improves training performance [https://arxiv.org/pdf/1602.01783v2.pdf].

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