Institute Talks

Talk

- 19 April 2018 • 11:00 12:00

- Preeya Khanna

- Heisenbergstr. 3, Room 2P4

Actions constitute the way we interact with the world, making motor disabilities such as Parkinson’s disease and stroke devastating. The neurological correlates of the injured brain are challenging to study and correct given the adaptation, redundancy, and distributed nature of our motor system. However, recent studies have used increasingly sophisticated technology to sample from this distributed system, improving our understanding of neural patterns that support movement in healthy brains, or compromise movement in injured brains. One approach to translating these findings to into therapies to restore healthy brain patterns is with closed-loop brain-machine interfaces (BMIs). While closed-loop BMIs have been discussed primarily as assistive technologies the underlying techniques may also be useful for rehabilitation.

Organizers: Katherine Kuchenbecker

Archived Talks

Talk

- 19 October 2017 • 10:00 11:00

- Slobodan Ilic and Mira Slavcheva

- PS Seminar Room (N3.022)

In this talk we will address the problem of 3D reconstruction of rigid and deformable objects from a single depth video stream. Traditional 3D registration techniques, such as ICP and its variants, are wide-spread and effective, but sensitive to initialization and noise due to the underlying correspondence estimation procedure. Therefore, we have developed SDF-2-SDF, a dense, correspondence-free method which aligns a pair of implicit representations of scene geometry, e.g. signed distance fields, by minimizing their direct voxel-wise difference. In its rigid variant, we apply it for static object reconstruction via real-time frame-to-frame camera tracking and posterior multiview pose optimization, achieving higher accuracy and a wider convergence basin than ICP variants. Its extension to scene reconstruction, SDF-TAR, carries out the implicit-to-implicit registration over several limited-extent volumes anchored in the scene and runs simultaneous GPU tracking and CPU refinement, with a lower memory footprint than other SLAM systems. Finally, to handle non-rigidly moving objects, we incorporate the SDF-2-SDF energy in a variational framework, regularized by a damped approximately Killing vector field. The resulting system, KillingFusion, is able to reconstruct objects undergoing topological changes and fast inter-frame motion in near-real time.

Organizers: Fatma Güney

IS Colloquium

- 02 October 2017 • 11:15 12:15

- Dominik Bach

Under acute threat, biological agents need to choose adaptive actions to survive. In my talk, I will provide a decision-theoretic view on this problem and ask, what are potential computational algorithms for this choice, and how are they implemented in neural circuits. Rational design principles and non-human animal data tentatively suggest a specific architecture that heavily relies on tailored algorithms for specific threat scenarios. Virtual reality computer games provide an opportunity to translate non-human animal tasks to humans and investigate these algorithms across species. I will discuss the specific challenges for empirical inference on underlying neural circuits given such architecture.

Organizers: Michel Besserve

Talk

- 02 October 2017 • 15:00 16:00

- Anton Van Den Hengel

- Aquarium

Visual Question Answering is one of the applications of Deep Learning that is pushing towards real Artificial Intelligence. It turns the typical deep learning process around by only defining the task to be carried out after the training has taken place, which changes the task fundamentally. We have developed a range of strategies for incorporating other information sources into deep learning-based methods, and the process taken a step towards developing algorithms which learn how to use other algorithms to solve a problem, rather than solving it directly. This talk thus covers some of the high-level questions about the types of challenges Deep Learning can be applied to, and how we might separate the things its good at from those that it’s not.

Organizers: Siyu Tang

- Prof. Sami Haddadin

- Main Seminar Room (N0.002)

Enabling robots for interaction with humans and unknown environments has been one of the primary goals of robotics research over decades. I will outline how human-centered robot design, nonlinear soft-robotics control inspired by human neuromechanics and physics grounded learning algorithms will let robots become a commodity in our near-future society. In particular, compliant and energy-controlled ultra-lightweight systems capable of complex collision handling enable high-performance human assistance over a wide variety of application domains. Together with novel methods for dynamics and skill learning, flexible and easy-to-use robotic power tools and systems can be designed. Recently, our work has led to the first next generation robot Franka Emika that has recently become commercially available. The system is able to safely interact with humans, execute and even learn sensitive manipulation skills, is affordable and designed as a distributed interconnected system.

Organizers: Eva Laemmerhirt

IS Colloquium

- 25 September 2017 • 11:15 12:15

- Amos Storkey

- Tübingen, MPI_IS Lecture Hall (ground floor)

In this talk I introduce the neural statistician as an approach for meta learning. The neural statistician learns to appropriately summarise datasets through a learnt statistic vector. This can be used for few shot learning, by computing the statistic vectors for the presented data, and using these statistics as context variables for one-shot classification and generation. I will show how we can generalise the neural statistician to a context aware learner that learns to characterise and combine independently learnt contexts. I will also demonstrate an approach for meta-learning data augmentation strategies. Acknowledgments: This work is joint work with Harri Edwards, Antreas Antoniou, and Conor Durkan.

Organizers: Philipp Hennig

- Prof. Amnon Shashua

- MPI_IS Stuttgart, Lecture Room 2 D5

The field of transportation is undergoing a seismic change with the coming introduction of autonomous driving. The technologies required to enable computer driven cars involves the latest cutting edge artificial intelligence algorithms along three major thrusts: Sensing, Planning and Mapping. Prof. Amnon Shashua, Co-founder and Chairman of Mobileye, will describe the challenges and the kind of machine learning algorithms involved, but will do that through the perspective of Mobileye’s activity in this domain.

- Georgios Arvanitidis

- S2 Seminar Room

The fundamental building block in many learning models is the distance measure that is used. Usually, the linear distance is used for simplicity. Replacing this stiff distance measure with a flexible one could potentially give a better representation of the actual distance between two points. I will present how the normal distribution changes if the distance measure respects the underlying structure of the data. In particular, a Riemannian manifold will be learned based on observations. The geodesic curve can then be computed—a length-minimizing curve under the Riemannian measure. With this flexible distance measure we get a normal distribution that locally adapts to the data. A maximum likelihood estimation scheme is provided for inference of the parameters mean and covariance, and also, a systematic way to choose the parameter defining the Riemannian manifold. Results on synthetic and real world data demonstrate the efficiency of the proposed model to fit non-trivial probability distributions.

Organizers: Philipp Hennig

- Prof. Dr. Hedvig Kjellström

- N3.022 / Aquarium

In this talk I will first outline my different research projects. I will then focus on the EACare project, a quite newly started multi-disciplinary collaboration with the aim to develop an embodied system, capable of carrying out neuropsychological tests to detect early signs of dementia, e.g., due to Alzheimer's disease. The system will use methods from Machine Learning and Social Robotics, and be trained with examples of recorded clinician-patient interactions. The interaction will be developed using a participatory design approach. I describe the scope and method of the project, and report on a first Wizard of Oz prototype.

- Yeara Kozlov

- Aquarium

Creating convincing human facial animation is challenging. Face animation is often hand-crafted by artists separately from body motion. Alternatively, if the face animation is derived from motion capture, it is typically performed while the actor is relatively still. Recombining the isolated face animation with body motion is non-trivial and often results in uncanny results if the body dynamics are not properly reflected on the face (e.g. cheeks wiggling when running). In this talk, I will discuss the challenges of human soft tissue simulation and control. I will then present our method for adding physical effects to facial blendshape animation. Unlike previous methods that try to add physics to face rigs, our method can combine facial animation and rigid body motion consistently while preserving the original animation as closely as possible. Our novel simulation framework uses the original animation as per-frame rest-poses without adding spurious forces. We also propose the concept of blendmaterials to give artists an intuitive means to control the changing material properties due to muscle activation.

Organizers: Timo Bolkart

IS Colloquium

- 21 August 2017 • 11:15 12:15

- Sanmi Koyejo

- Empirical Inference meeting room (MPI-IS building, 4th floor)

Performance metrics are a key component of machine learning systems, and are ideally constructed to reflect real world tradeoffs. In contrast, much of the literature simply focuses on algorithms for maximizing accuracy. With the increasing integration of machine learning into real systems, it is clear that accuracy is an insufficient measure of performance for many problems of interest. Unfortunately, unlike accuracy, many real world performance metrics are non-decomposable i.e. cannot be computed as a sum of losses for each instance. Thus, known algorithms and associated analysis are not trivially extended, and direct approaches require expensive combinatorial optimization. I will outline recent results characterizing population optimal classifiers for large families of binary and multilabel classification metrics, including such nonlinear metrics as F-measure and Jaccard measure. Perhaps surprisingly, the prediction which maximizes the utility for a range of such metrics takes a simple form. This results in simple and scalable procedures for optimizing complex metrics in practice. I will also outline how the same analysis gives optimal procedures for selecting point estimates from complex posterior distributions for structured objects such as graphs. Joint work with Nagarajan Natarajan, Bowei Yan, Kai Zhong, Pradeep Ravikumar and Inderjit Dhillon.

Organizers: Mijung Park