Institute Talks

Frederick Eberhardt - TBA

IS Colloquium
  • 03 July 2017 • 11:15 12:15
  • Frederick Eberhardt
  • Max Planck House Lecture Hall

Organizers: Sebastian Weichwald

  • Fabien Lotte
  • Max Planck House Lecture Hall

Brain-Computer Interfaces (BCIs) are systems that can translate brain activity patterns of a user into messages or commands for an interactive application. Such brain activity is typically measured using Electroencephalography (EEG), before being processed and classified by the system. EEG-based BCIs have proven promising for a wide range of applications ranging from communication and control for motor impaired users, to gaming targeted at the general public, real-time mental state monitoring and stroke rehabilitation, to name a few. Despite this promising potential, BCIs are still scarcely used outside laboratories for practical applications. The main reason preventing EEG-based BCIs from being widely used is arguably their poor usability, which is notably due to their low robustness and reliability, as well as their long training times. In this talk I present some of our research aimed at addressing these points in order to make EEG-based BCIs usable, i.e., to increase their efficacy and efficiency. In particular, I will present a set of contributions towards this goal 1) at the user training level, to ensure that users can learn to control a BCI efficiently and effectively, and 2) at the usage level, to explore novel applications of BCIs for which the current reliability can already be useful, e.g., for neuroergonomics or real-time brain activity and mental state visualization.


  • Ralf Nagel
  • AGBS Seminar Room

The predictive simulation of engineering systems increasingly rests on the synthesis of physical models and experimental data. In this context, Bayesian inference establishes a framework for quantifying the encountered uncertainties and fusing the available information. A summary and discussion of some recently emerged methods for uncertainty propagation (polynomial chaos expansions) and related MCMC-free techniques for posterior computation (spectral likelihood expansions, optimal transportation theory) is presented.

Organizers: Philipp Hennig


Deep Learning and its Relationship with Time

Talk
  • 08 December 2016 • 11:00 12:00
  • Laura Leal-Taixé
  • MRZ Seminar Room

In this talk I am going to present the work we have been doing at the Computer Vision Lab of the Technical University of Munich which started as an attempt to better deal with videos (and therefore the time domain) within neural network architectures.

Organizers: Joel Janai


  • Kathleen Robinette
  • MRZ Seminar Room

Kathleen is the creator of the well-known CAESAR anthropomorphic dataset and is an expert on body shape and apparel fit.

Organizers: Javier Romero


Intelligent control of uncertain underactuated mechanical systems

Talk
  • 01 December 2016 • 11:00 - 01 November 2016 • 12:00
  • Wallace M. Bessa
  • AMD Seminar Room (Paul-Ehrlich-Str. 15, 1rst floor)

Underactuated mechanical systems (UMS) play an essential role in several branches of industrial activity and their application scope ranges from robotic manipulators and overhead cranes to aerospace vehicles and watercrafts. Despite this broad spectrum of applications, the problem of designing accurate controllers for underactuated systems is, however, much more tricky than for fully actuated ones. Moreover, the dynamic behavior of an UMS is frequently uncertain and highly nonlinear, which in fact makes the design of control schemes for such systems a challenge for conventional and well established methods. In this talk, it will be shown that intelligent algorithms, such as fuzzy logic and artificial neural networks, could be combined with nonlinear control techniques (feedback linearization or sliding modes) in order to improve both set-point regulation and trajectory tracking of uncertain underactuated mechanical systems.

Organizers: Sebastian Trimpe


  • Carsten Rother
  • MRZ seminar room

In this talk I will present the portfolio of work we conduct in our lab. Herby, I will present three recent body of work in more detail. This is firstly our work on learning 6D Object Pose estimation and Camera localizing from RGB or RGBD images. I will show that by utilizing the concepts of uncertainty and learning to score hypothesis, we can improve the state of the art. Secondly, I will present a new approach for inferring multiple diverse labeling in a graphical model. Besides guarantees of an exact solution, our method is also faster than existing techniques. Finally, I will present a recent work in which we show that popular Auto-context Decision Forests can be mapped to Deep ConvNets for Semantic Segmentation. We use this to detect the spine of a zebrafish, in case when little training data is available.

Organizers: Aseem Behl


  • Dr. Bogdan Savchynskyy
  • MRZ seminar room

We propose a new computational framework for combinatorial problems arising in machine learning and computer vision. This framework is a special case of Lagrangean (dual) decomposition, but allows for efficient dual ascent (message passing) optimization. In a sense, one can understand both the framework and the optimization technique as a generalization of those for standard undirected graphical models (conditional random fields). We will make an overview of our recent results and plans for the nearest future.

Organizers: Aseem Behl


  • Bogdan Savchynskyy
  • Mrz Seminar Room (room no. 0.A.03)

We propose a new computational framework for combinatorial problems arising in machine learning and computer vision. This framework is a special case of Lagrangean (dual) decomposition, but allows for efficient dual ascent (message passing) optimization. In a sense, one can understand both the framework and the optimization technique as a generalization of those for standard undirected graphical models (conditional random fields). We will make an overview of our recent results and plans for the nearest future.

Organizers: Aseem Behl


Monte Carlo with determinantal point processes

Talk
  • 02 November 2016 • 15:00 16:00
  • Rémi Bardenet
  • AGBS Seminar room (Spemannstr. 38)

In this talk, we show that using repulsive random variables, it is possible to build Monte Carlo methods that converge faster than vanilla Monte Carlo. More precisely, we build estimators of integrals, the variance of which decreases as $N^{-1-1/d}$, where $N$ is the number of integrand evaluations, and $d$ is the ambient dimension. To do so, we propose stochastic numerical quadratures involving determinantal point processes (DPPs) associated to multivariate orthogonal polynomials. The proposed method can be seen as a stochastic version of Gauss' quadrature, where samples from a determinantal point process replace zeros of orthogonal polynomials. Furthermore, integration with DPPs is close in spirit to randomized quasi-Monte Carlo methods, leveraging repulsive point processes to ensure low discrepancy samples. The talk is based on the following preprint https://arxiv.org/abs/1605.00361

Organizers: Alexandra Gessner


  • Hedvig Kjellström
  • MRZ Seminar Room

In this talk I will first outline my different research projects. I will then focus on one project with applications in Health, and introduce the Inter-Battery Topic Model (IBTM). Our approach extends traditional topic models by learning a factorized latent variable representation. The structured representation leads to a model that marries benefits traditionally associated with a discriminative approach, such as feature selection, with those of a generative model, such as principled regularization and ability to handle missing data. The factorization is provided by representing data in terms of aligned pairs of observations as different views. This provides means for selecting a representation that separately models topics that exist in both views from the topics that are unique to a single view. This structured consolidation allows for efficient and robust inference and provides a compact and efficient representation.