In an effort to improve the performance of deep neural networks in data-scarce, non-i.i.d., or unsupervised settings, much recent research has been devoted to encoding invariance under symmetry transformations into neural network architectures. We treat the neural network input and output as random variables, and consider group invariance from the perspective of probabilistic symmetry. Drawing on tools from probability and statistics, we establish a link between functional and probabilistic symmetry, and obtain functional representations of probability distributions that are invariant or equivariant under the action of a compact group. Those representations characterize the structure of neural networks that can be used to represent such distributions and yield a general program for constructing invariant stochastic or deterministic neural networks. We develop the details of the general program for exchangeable sequences and arrays, recovering a number of recent examples as special cases. This is work in collaboration with Yee Whye Teh. https://arxiv.org/abs/1901.06082
Organizers: Isabel Valera
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 (both political scientists). Building on this tradition and integrating ideas from deep learning and Hegel's philosophy of logic a new perspective on causality might arise.
Organizers: Philipp Geiger
We present a novel probabilistic integrator for ordinary differential equations (ODEs) which allows for uncertainty quantification of the numerical error . 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 integrals or the symplecticity of the flow. Finally, we present a procedure to incorporate our probabilistic solver into the frame of Bayesian inference inverse problems, showing how inaccurate posterior concentrations given by deterministic methods can be corrected by a probabilistic interpretation of the numerical solution.
Organizers: Hans Kersting
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 predictable and interpretable high-order interactions among biomolecules.
Organizers: Michel Besserve
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 the way it will touch topics such as the heuristics hypothesis and learning of visual representations. The connecting theme will be that, from the switching of visuomotor behavior in response to changing task-constraints down to cortical visual representations in V1, action and perception are inseparably intertwined in an ambiguous and uncertain world
Organizers: Betty Mohler
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 and real-world demands via task-driven modelling.
Organizers: Michel Besserve
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. 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.
Organizers: Metin Sitti
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 image denoising and segmentation methods to produce a point cloud approximating the vocal tract surface. In this context, I will also discuss how palatal contacts of the tongue can be handled, i.e., situations where the tongue touches the palate and thus no tongue boundary is visible. Afterwards, template matching is used to derive a mesh representation of the tongue from this cloud. The acquired meshes are finally used to construct a multilinear model.
Organizers: Timo Bolkart
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.
Organizers: Philipp Hennig
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 of specific tasks in a structured laboratory settings, our approach involves participants completing many hours of self-selected household chores in their own homes while wearing a head mounted video camera. I will discuss how we have addressed the processing of such a large and unstructured data set, in addition to our current findings. Complementary to my work in prosthetics, I will also discuss my work on several novel robotic grippers which aim to enhance the grasping, manipulation and object identification capabilities of robotic systems. These grippers implement underactuated designs, machine learning approaches or variable friction surfaces to provide low-cost, model-free and easily reproducible solutions to what have been traditionally been considered complex problems in robotic manipulation, i.e. stable grasp acquisition, fast tactile object recognition and within-hand object manipulation. Finally, I will present a brief overview of my efforts designing and testing shape-changing haptic interfaces, a largely unexplored feedback modality that I believe has huge potential for discretely communicating information to people with and without sensory impairments. This technology has been implemented in a pedestrian navigation system and evaluated in a variety of scenarios, including a large scale immersive theatre production with visually impaired artistic collaborators and almost 100 participants.
Organizers: Katherine J. Kuchenbecker
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 focused on low-level object properties (such as curvature, or surface granularity) such that little is known on how the human actually forms a high-level multisensory representation of objects. Here, I will review research from our lab that investigates how the human brain processes shape using input from vision and touch. Using a large variety of novel, 3D-printed shapes we were able to show that touch is actually equally good at shape processing than vision, suggesting a common, multisensory representation of shape. We next conducted a series of imaging experiments (using anatomical, functional, and white-matter analyses) that chart the brain networks that process this shape representation. I will conclude the talk with a brief medley of other haptics-related research in the lab, including robot learning, braille, and haptic face recognition.
Organizers: Katherine J. Kuchenbecker