Events & Talks

Physical Intelligence Talk Dr. Islam S. M. Khali 07-05-2018 Mechanical Rubbing of Blood Clots Using Helical Robots under Ultrasound Guidance. Mechanical removal of blood clots is a promising approach towards the treatment of vascular diseases caused by the pathological clot formation in the circulatory system. These clots can form and travel to deep seated regions in the circulatory system, and result in significant problems as blood flow past the clot is obstructed. A microscopi-cally small helical microrobot offers great promise in the minimally-invasive removal of these clots. These helical microrobots are powered and controlled remotely using externally-applied magnetic fields for motion in two- and three-dimensional spac... Metin Sitti

Dynamic Locomotion Talk Daniel Renjewski 04-05-2018 Daniel Renjewski: bipedal gait mechanisms Daniel Renjewski presents research in bipedal gait mechanisms: 'Passive mechanisms for increased power and efficiency in bipedal gait’

Physical Intelligence IS Colloquium Dr. Yiğit Mengüç 30-04-2018 Material Robotics – How new soft composites, mechanisms, and manufacturing techniques make robots better. Incredible biological capabilities have emerged through evolution. Of special note is the material intelligence that defines the bodies of living things, blurring the line between brain and body. Material robotics research takes the approach of imbuing power, control, sensing, and actuation into all aspects of a (primarily soft) robot body. In this talk, the research topics of material robotics currently underway in the mLab at Oregon State University will be presented. Soft active materials designed and researched in the mLab include liquid metal, biodegradable elastomers, and electroactiv... Metin Sitti

Perceiving Systems Talk JP Lewis 27-04-2018 Constructing Artificial Characters - Traditional versus Deep Learning Approaches The definition of art has been debated for more than 1000 years, and continues to be a puzzle. While scientific investigations offer hope of resolving this puzzle, machine learning classifiers that discriminate art from non-art images generally do not provide an explicit definition, and brain imaging and psychological theories are at present too coarse to provide a formal characterization. In this work, rather than approaching the problem using a machine learning approach trained on existing artworks, we hypothesize that art can be defined in terms of preexisting properties of the visual co... Michael Black

Perceiving Systems Talk Dr. Cordelia Schmid 26-04-2018 Automatic Understanding of the Visual World One of the central problems of artificial intelligence is machine perception, i.e., the ability to understand the visual world based on input from sensors such as cameras. In this talk, I will present recent progress with respect to data generation using weak annotations, motion information and synthetic data. I will also discuss our recent results for action recognition, where human tubes and tubelets have shown to be successful. Our tubelets moves away from state-of-the-art frame based approaches and improve classification and localization by relying on joint information from several fram... Ahmed Osman

Haptic Intelligence Talk Preeya Khanna 19-04-2018 Brain-Machine Interfaces as Rehabilitative Tools for Motor Disorders 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 therapi... Katherine J. Kuchenbecker

Talk Erwan Scornet 18-04-2018 Consistency and minimax rates of random forests The recent and ongoing digital world expansion now allows anyone to have access to a tremendous amount of information. However collecting data is not an end in itself and thus techniques must be designed to gain in-depth knowledge from these large data bases. Mara Cascianelli

Perceiving Systems Talk Alexander Mathis 17-04-2018 Markerless Tracking of User-Defined Features with Deep Learning Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, yet markers are intrusive (especially for smaller animals), and the number and location of the markers must be determined a priori. Here, we present a highly efficient method for markerles... Melanie Feldhofer

Haptic Intelligence IS Colloquium Jan Peters 13-04-2018 Machine Learning for Tactile Manipulation Today’s robots have motor abilities and sensors that exceed those of humans in many ways: They move more accurately and faster; their sensors see more and at a higher precision and in contrast to humans they can accurately measure even the smallest forces and torques. Robot hands with three, four, or five fingers are commercially available, and, so are advanced dexterous arms. Indeed, modern motion-planning methods have rendered grasp trajectory generation a largely solved problem. Still, no robot to date matches the manipulation skills of industrial assembly workers despite that manipulati... Katherine J. Kuchenbecker

Perceiving Systems Talk Gül Varol 10-04-2018 BodyNet: Volumetric Inference of 3D Human Body Shapes Human shape estimation is an important task for video editing, animation and fashion industry. Predicting 3D human body shape from natural images, however, is highly challenging due to factors such as variation in human bodies, clothing and viewpoint. Prior methods addressing this problem typically attempt to fit parametric body models with certain priors on pose and shape. In this work we argue for an alternative representation and propose BodyNet, a neural network for direct inference of volumetric body shape from a single image. BodyNet is an end-to-end trainable network that benefits fr...

Haptic Intelligence Talk Dr. Vincent Berenz 04-04-2018 A New Perspective on Usability Applied to Robotics For many service robots, reactivity to changes in their surroundings is a must. However, developing software suitable for dynamic environments is difficult. Existing robotic middleware allows engineers to design behavior graphs by organizing communication between components. But because these graphs are structurally inflexible, they hardly support the development of complex reactive behavior. To address this limitation, we propose Playful, a software platform that applies reactive programming to the specification of robotic behavior. The front-end of Playful is a scripting language which is... Katherine J. Kuchenbecker Mayumi Mohan Alexis Block

Perceiving Systems Talk Omar Costilla Reyes 28-03-2018 Deep Residual Neural Networks for Robust Footstep Recognition Human footsteps can provide a unique behavioural pattern for robust biometric systems. Traditionally, security systems have been based on passwords or security access cards. Biometric recognition deals with the design of security systems for automatic identification or verification of a human subject (client) based on physical and behavioural characteristics. In this talk, I will present spatio-temporal raw and processed footstep data representations designed and evaluated on deep machine learning models based on a two-stream resnet architecture, by using the SFootBD database the largest f... Dimitris Tzionas

Perceiving Systems Talk Silvia Zuffi 27-03-2018 Capturing animal 3D articulated shape and appearance from images Animals are widespread in nature and the analysis of their shape and motion is of importance in many fields and industries. Modeling 3D animal shape, however, is difficult because the 3D scanning methods used to capture human shape are not applicable to wild animals or natural settings. In our previous SMAL model, we learn animal shape from toys figurines, but toys are limited in number and realism, and not every animal is sufficiently popular for there to be realistic toys depicting it. What is available in large quantities are images and videos of animals from nature photographs, anim...

Probabilistic Numerics Talk Sergio Pascual Díaz 23-03-2018 Deep Gaussian Processes and applications in Bayesian Optimisation My plan is to present the motivation behind Deep GPs as well as some of the current approximate inference schemes available with their limitations. Then, I will explain how Deep GPs fit into the BayesOpt framework and the specific problems they could potentially solve. Philipp Hennig

Empirical Inference IS Colloquium Patrick Bajari 23-03-2018 The Impact of Big Data on Firm Performance: an Empirical Investigation In academic and policy circles, there has been considerable interest in the impact of “big data” on firm performance. We examine the question of how the amount of data impacts the accuracy of Machine Learned models of weekly retail product forecasts using a proprietary data set obtained from Amazon. We examine the accuracy of forecasts in two relevant dimensions: the number of products (N), and the number of time periods for which a product is available for sale (T). Theory suggests diminishing returns to larger N and T, with relative forecast errors diminishing at rate 1/sqrt(N) + 1/sqrt(... Michel Besserve Michael Hirsch

IS Colloquium Simon Hegelich 12-03-2018 Political Science and Data Science: What we can learn from each other Political science is integrating computational methods like machine learning into its own toolbox. At the same time the awareness rises that the utilization of machine learning algorithms in our daily life is a highly political issue. These two trends - the integration of computational methods into political science and the political analysis of the digital revolution - form the ground for a new transdisciplinary approach: political data science. Interestingly, there is a rich tradition of crossing the borders of the disciplines, as can be seen in the works of Paul Werbos and Herbert Simon ... Philipp Geiger

Probabilistic Numerics Talk Giacomo Garegnani 06-03-2018 Uncertainty quantification of numerical errors in geometric integration via random time steps We present a novel probabilistic integrator for ordinary differential equations (ODEs) which allows for uncertainty quantification of the numerical error [1]. In particular, we randomise the time steps and build a probability measure on the deterministic solution, which collapses to the true solution of the ODE with the same rate of convergence as the underlying deterministic scheme. The intrinsic nature of the random perturbation guarantees that our probabilistic integrator conserves some geometric properties of the deterministic method it is built on, such as the conservation of first in... Hans Kersting

Empirical Inference IS Colloquium Bin Yu 05-03-2018 Three principles of data science: predictability, stability, and computability In this talk, I'd like to discuss the intertwining importance and connections of three principles of data science in the title. They will be demonstrated in the context of two collaborative projects in neuroscience and genomics, respectively. The first project in neuroscience uses transfer learning to integrate fitted convolutional neural networks (CNNs) on ImageNet with regression methods to provide predictive and stable characterizations of neurons from the challenging primary visual cortex V4. The second project proposes iterative random forests (iRF) as a stablized RF to seek predictab... Michel Besserve

Perceiving Systems Talk Prof. Constantin Rothkopf 28-02-2018 Perception and action are inseparably intertwined: computational and experimental evidence Active vision has long put forward the idea, that visual sensation and our actions are inseparable, especially when considering naturalistic extended behavior. Further support for this idea comes from theoretical work in optimal control, which demonstrates that sensing, planning, and acting in sequential tasks can only be separated under very restricted circumstances. The talk will present experimental evidence together with computational explanations of human visuomotor behavior in tasks ranging from classic psychophysical detection tasks to ball catching and visuomotor navigation. Along t... Betty Mohler

Empirical Inference Talk Aljoscha Leonhardt 27-02-2018 A naturalistic perspective on optic flow processing in the fly Optic flow offers a rich source of information about an organism’s environment. Flies, for instance, are thought to make use of motion vision to control and stabilise their course during acrobatic airborne manoeuvres. How these computations are implemented in neural hardware and how such circuits cope with the visual complexity of natural scenes, however, remain open questions. This talk outlines some of the progress we have made in unraveling the computational substrate underlying optic flow processing in Drosophila. In particular, I will focus on our efforts to connect neural mechanisms a... Michel Besserve

Physical Intelligence Talk Professor Rahmi Oklu 26-02-2018 Patient Inspired Engineering: Problem, device, solution Minimally invasive approaches to the treatment of vascular diseases are constantly evolving. These diseases are among the most prevalent medical problems today including stroke, myocardial infarction, pulmonary emboli, hemorrhage and aneurysms. </br>I will review current approaches to vascular embolization and thrombosis, the challenges they pose and the limitations of current devices and end with patient inspired engineering approaches to the treatment of these conditions. Metin Sitti

Perceiving Systems Talk Alexander Hewer 20-02-2018 Deriving a Tongue Model from MRI Data The tongue plays a vital part in everyday life where we use it extensively during speech production. Due to this importance, we want to derive a parametric shape model of the tongue. This model enables us to reconstruct the full tongue shape from a sparse set of points, like for example motion capture data. Moreover, we can use such a model in simulations of the vocal tract to perform articulatory speech synthesis or to create animated virtual avatars. In my talk, I describe a framework for deriving such a model from MRI scans of the vocal tract. In particular, this framework uses im... Timo Bolkart

Talk Randolf Scholz 19-02-2018 Nonstandard Analysis - The Comeback of Infinitesimals The early Calculus of Newton and Leibniz made heavy use of infinitesimal quantities and flourished for over a hundred years until it was superseded by the more rigorous epsilon-delta formalism. It took until the 1950's for A. Robinson to find a proper way to construct a number system containing actual infinitesimals -- the Hyperreals *|R. This talk outlines their construction and possible applications in modern analysis. Philipp Hennig

Haptic Intelligence Talk Dr. Adam Spiers 15-02-2018 Studying and Engineering Manipulation and Haptic Perception in Humans and Robots This talk will focus on three topics of my research at Yale University, which centers on themes of human and robotic manipulation and haptic perception. My major research undertaking at Yale has involved running a quantitative study of daily upper-limb prosthesis use in unilateral amputees. This work aims to better understand the techniques employed by long-term users of artificial arms and hands in order to inform future prosthetic device design and therapeutic interventions. While past attempts to quantify prosthesis-use have implemented either behavioral questionnaires or observations ... Katherine J. Kuchenbecker

Haptic Intelligence IS Colloquium Prof. Christian Wallraven 13-02-2018 Vision and Haptics: a Cognitive and Computational Investigation About How We Perceive the World Already starting at birth, humans integrate information from several sensory modalities in order to form a representation of the environment - such as when a baby explores, manipulates, and interacts with objects. The combination of visual and touch information is one of the most fundamental sensory integration processes, as touch information (such as body-relative size, shape, texture, material, temperature, and weight) can easily be linked to the visual image, thereby providing a grounding for later visual-only recognition. Previous research on such integration processes has so far mainly... Katherine J. Kuchenbecker

Workshop 08-02-2018 - 09-02-2018 Second Max Planck ETH Workshop on Learning Control After a successful first edition in 2015, we are pleased to announce the second workshop on Learning Control within the Max Planck ETH Center for Learning Systems. The workshop will take place February 8-9 2018 at ETH Zurich. We cordially invite all researchers from ETH Zürich and MPI-IS interested in the area of Learning Control to participate and actively contribute to this workshop. Sebastian Trimpe Georg Martius Melanie Zeilinger

Haptic Intelligence Talk Haliza Mat Husin 19-01-2018 Maternal Weight and Metabolism Related to Fetal Autonomic Nervous System Background: Pre-pregnancy obesity and inadequate maternal weight gain during pregnancy can lead to adverse effects in the newborn but also to metabolic, cardiovascular and even neurological diseases in older ages of the offspring. Heart activity can be used as a proxy for the activity of the autonomic nervous system (ANS). The aim of this study is to evaluate the effect of pre-pregnancy weight, maternal weight gain and maternal metabolism on the ANS of the fetus in healthy pregnancies. Katherine J. Kuchenbecker

Autonomous Vision Talk Vagia Tsiminaki 15-12-2017 Appearance Modeling for 4D Multi-view Representations The emergence of multi-view capture systems has yield a tremendous amount of video sequences. The task of capturing spatio-temporal models from real world imagery (4D modeling) should arguably benefit from this enormous visual information. In order to achieve highly realistic representations both geometry and appearance need to be modeled in high precision. Yet, even with the great progress of the geometric modeling, the appearance aspect has not been fully explored and visual quality can still be improved. I will explain how we can optimally exploit the redundant visual information of ... Despoina Paschalidou

Empirical Inference Talk Robert Peharz 06-12-2017 Sum-Product Networks for Probabilistic Modeling Probabilistic modeling is the method of choice when it comes to reasoning under uncertainty. However, one of the main practical downsides of probabilistic models is that inference, i.e. the process of using the model to answer statistical queries, is notoriously hard in general. This led to a common folklore that probabilistic models which allow exact inference are necessarily simplistic and undermodel any practical task. In this talk, I will present sum-product networks (SPNs), a recently proposed architecture representing a rich and expressive class of probability distributions, which als...

Perceiving Systems Talk Dr. Gerard Pons-Moll 30-11-2017 Reconstructing and Perceiving Humans in Motion For man-machine interaction it is crucial to develop models of humans that look and move indistinguishably from real humans. Such virtual humans will be key for application areas such as computer vision, medicine and psychology, virtual and augmented reality and special effects in movies. Currently, digital models typically lack realistic soft tissue and clothing or require time-consuming manual editing of physical simulation parameters. Our hypothesis is that better and more realistic models of humans and clothing can be learned directly from real measurements coming from 4D scans, ima... Melanie Feldhofer

Haptic Intelligence IS Colloquium Professor Brent Gillespie 20-11-2017 Extending the Reafference and Internal Model Principles to Support Physical Human-Robot Interaction Relative to most robots and other machines, the human body is soft, its actuators compliant, and its control quite forgiving. But having a body that bends under load seems like a bad set-up for motor dexterity: the brain is faced with controlling more rather than fewer degrees of freedom. Undeniably, though, the soft body approach leads to superior solutions. Robots are putzes by comparison! While de-putzifying robots (perhaps by making them softer) is an endeavor I will discuss to some degree, in this talk I will focus on the design of robots intended to work cooperatively with humans, usi... Katherine J. Kuchenbecker

Autonomous Vision Talk Christoph Mayer 17-11-2017 Operator splitting: a versatile framework for variational image processing tasks. Variational image processing translates image processing tasks into optimisation problems. The practical success of this approach depends on the type of optimisation problem and on the properties of the ensuing algorithm. A recent breakthrough was to realise that old first-order optimisation algorithms based on operator splitting are particularly suited for modern data analysis problems. Operator splitting techniques decouple complex optimisation problems into many smaller and simpler sub-problems. In this talk I will revise the variational segmentation problem and a common family of al... Benjamin Coors

Perceiving Systems Symposium 29-10-2017 - 01-11-2017 Scenes from Video III This is the third in a series of invitation-only workshops held after ICCV. SfV brings together experts on image and video understanding, machine learning, and 3D scene analysis. In so doing, we hope to draw several lines of research together to address the problem of extracting both physical and semantic information from video. Michael Black

Autonomous Motion Talk Jens Kober 26-10-2017 Learning Complex Robot-Environment Interactions The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Reinforcement learning and imitation learning are two different but complimentary machine learning approaches commonly used for learning motor skills. Dieter Büchler

Empirical Inference IS Colloquium Simon Lacoste-Julien 23-10-2017 Modern Optimization for Structured Machine Learning Machine learning has become a popular application domain for modern optimization techniques, pushing its algorithmic frontier. The need for large scale optimization algorithms which can handle millions of dimensions or data points, typical for the big data era, have brought a resurgence of interest for first order algorithms, making us revisit the venerable stochastic gradient method [Robbins-Monro 1951] as well as the Frank-Wolfe algorithm [Frank-Wolfe 1956]. In this talk, I will review recent improvements on these algorithms which can exploit the structure of modern machine learning appro... Philipp Hennig

Autonomous Motion Talk Arunkumar Byravan 23-10-2017 Structured Deep Visual Dynamics Models for Robot Manipulation The ability to predict how an environment changes based on forces applied to it is fundamental for a robot to achieve specific goals. Traditionally in robotics, this problem is addressed through the use of pre-specified models or physics simulators, taking advantage of prior knowledge of the problem structure. While these models are general and have broad applicability, they depend on accurate estimation of model parameters such as object shape, mass, friction etc. On the other hand, learning based methods such as Predictive State Representations or more recent deep learning approaches have... Franzi Meier

Autonomous Vision Talk Michiel Vlaminck 20-10-2017 3D lidar mapping: an accurate and performant approach In my talk I will present my work regarding 3D mapping using lidar scanners. I will give an overview of the SLAM problem and its main challenges: robustness, accuracy and processing speed. Regarding robustness and accuracy, we investigate a better point cloud representation based on resampling and surface reconstruction. Moreover, we demonstrate how it can be incorporated in an ICP-based scan matching technique. Finally, we elaborate on globally consistent mapping using loop closures. Regarding processing speed, we propose the integration of our scan matching in a multi-resolution scheme an... Simon Donne

Autonomous Motion Talk Michael and Susan Leigh Anderson 20-10-2017 Machine Ethics We argue that ethically significant behavior of autonomous systems should be guided by explicit ethical principles determined through a consensus of ethicists. Such a consensus is likely to emerge in many areas in which autonomous systems are apt to be deployed and for the actions they are liable to undertake, as we are more likely to agree on how machines ought to treat us than on how human beings ought to treat one another. Given such a consensus, particular cases of ethical dilemmas where ethicists agree on the ethically relevant features and the right course of action can be used to hel... Vincent Berenz

Autonomous Vision Talk Slobodan Ilic and Mira Slavcheva 19-10-2017 SDF-2-SDF: 3D Reconstruction of Rigid and Deformable Objects from RGB-D Videos 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 ... Fatma Güney

Empirical Inference IS Colloquium Dominik Bach 02-10-2017 Algorithms for survival: a decision-theoretic perspective on adaptive action under threat 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 ... Michel Besserve

Perceiving Systems Talk Anton Van Den Hengel 02-10-2017 Visual Question Answering and why we’re asking the wrong question 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 ... Siyu Tang

Talk Prof. Sami Haddadin 27-09-2017 The Gentle Robot 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 s... Eva Laemmerhirt

Probabilistic Numerics IS Colloquium Amos Storkey 25-09-2017 Meta-learning statistics and augmentations for few shot learning 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 augmen... Philipp Hennig

IS Colloquium Prof. Amnon Shashua 18-09-2017 The Three Pillars of Fully Autonomous Driving 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. Michael Black

Max Planck Lecture Prof. Amnon Shashua 18-09-2017 The Three Pillars of Fully Autonomous Driving 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.

Autonomous Vision Event 07-09-2017 - 08-09-2017 AVG Summer Retreat

Probabilistic Numerics Talk Georgios Arvanitidis 05-09-2017 A locally Adaptive Normal Distribution 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... Philipp Hennig

Talk Prof. Dr. Hedvig Kjellström 25-08-2017 Developing an embodied agent to detect early signs of dementia 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 repo...

Perceiving Systems Talk Yeara Kozlov 23-08-2017 Physical Blendshapes - Controllable Physics for Human Faces 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 ... Timo Bolkart

Empirical Inference IS Colloquium Sanmi Koyejo 21-08-2017 Metrics Matter, Examples from Binary and Multilabel Classification 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 t... Mijung Park

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