Writing and maintaining programs for robots poses some interesting challenges. It is hard to generalize them, as their targets are more than computing platforms. It can be deceptive to see them as input to output mappings, as interesting environments result in unpredictable inputs, and mixing reactive and deliberative behavior make intended outputs hard to define. Given the wide and fragmented landscape of components, from hardware to software, and the parties involved in providing and using them, integration is also a non-trivial aspect. The talk will illustrate the work ongoing at Fraunhofer IPA to tackle these challenges, how Open Source is its common trait, and how this translates into the industrial field thanks to the ROS-Industrial initiative.
Organizers: Vincent Berenz
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
How do young children learn so much about the world, and so efficiently? This talk presents the recent studies investigating theoretically and empirically how children actively seek information in their physical and social environments as evidence to test and dynamically revise their hypotheses and theories over time. In particular, it will focus on how children adapt their active learning strategies. such as question-asking and explorative behavior, in response to the task characteristics, to the statistical structure of the hypothesis space, and to the feedback received. Such adaptiveness and flexibility is crucial to achieve efficiency in situations of uncertainty, when testing alternative hypotheses, making decisions, drawing causal inferences and solving categorization tasks.
Neural networks have taken the world of computing in general and AI in particular by storm. But in the future, AI will need to revisit generative models. There are several reasons for this – system robustness, precision, transparency, and the high cost of labelling data. This is particularly true of perceptual AI, as needed for autonomous vehicles, where also the need for simulators and the need to confront novel situations, also will demand generative, probabilistic models.
Recently, deep learning proved to be successful also on low level vision tasks such as stereo matching. Another recent trend in this latter field is represented by confidence measures, with increasing effectiveness when coupled with random forest classifiers or CNNs. Despite their excellent accuracy in outliers detection, few other applications rely on them. In the first part of the talk, we'll take a look at the latest proposal in terms of confidence measures for stereo matching, as well as at some novel methodologies exploiting these very accurate cues. In the second part, we'll talk about GC-net, a deep network currently representing the state-of-the-art on the KITTI datasets, and its extension to motion stereo processing.
Organizers: Yiyi Liao
Bioelectronics integrates principles of electrical engineering and materials science to biology, medicine and ultimately health. Soft bioelectronics focus on designing and manufacturing electronic devices with mechanical properties close to those of the host biological tissue so that long-term reliability and minimal perturbation are induced in vivo and/or truly wearable systems become possible. We illustrate the potential of this soft technology with examples ranging from prosthetic tactile skins to soft multimodal neural implants.
Organizers: Diana Rebmann
Vaccine refusal can lead to outbreaks of previously eradicated diseases and is an increasing problem worldwide. Vaccinating decisions exemplify a complex, coupled system where vaccinating behavior and disease dynamics influence one another. Complex systems often exhibit characteristic dynamics near a tipping point to a new dynamical regime. For instance, critical slowing down -- the tendency for a system to start `wobbling'-- can increase close to a tipping point. We used a linear support vector machine to classify the sentiment of geo-located United States and California tweets concerning measles vaccination from 2011 to 2016. We also extracted data on internet searches on measles from Google Trends. We found evidence for critical slowing down in both datasets in the years before and after the 2014-15 Disneyland, California measles outbreak, suggesting that the population approached a tipping point corresponding to widespread vaccine refusal, but then receded from the tipping point in the face of the outbreak. A differential equation model of coupled behaviour-disease dynamics is shown to illustrate the same patterns. We conclude that studying critical phenomena in online social media data can help us develop analytical tools based on dynamical systems theory to identify populations at heightened risk of widespread vaccine refusal.
Organizers: Diana Rebmann
This talk will look at hardware-based means of assembling, controlling and driving systems at the smallest of scales, including those that can become autonomous. I will show that insights from physics, chemistry and material engineering can be used to permit the simplification and miniaturization of otherwise bulky systems and that this can give rise to new technologies. One of the technologies we have invented may also permit the development of new imaging devices.
In multi-task learning, a learner is given a collection of prediction tasks and needs to solve all of them. In contrast to previous work, that required that annotated training data must be available for all tasks, I will talk about a new setting, in which for some tasks, potentially most of them, only unlabeled training data is available. Consequently, to solve all tasks, information must be transfered between tasks with labels and tasks without labels. Focussing on an instance-based transfer method I will consider two variants of this setting: when the set of labeled tasks is fixed, and when it can be actively selected by the learner. I will discuss a generalization bound that covers both scenarios and an algorithm, that follows from it, for making the choice of labeled tasks (in the active case) and for transferring information between the tasks in a principled way. I will also show results of some experiments that illustrate the effectiveness of the algorithm.
Organizers: Georg Martius
This talk draws three parallels between classical algebraic quadrature rules, that are exact for polynomials of low degree, and kernel (or Bayesian) quadrature rules: i) Computational efficiency. Construction of scalable multivariate algebraic quadrature rules is challenging whereas kernel quadrature necessitates solving a linear system of equations, quickly becoming computationally prohibitive. Fully symmetric sets and Smolyak sparse grids can be used to solve both problems. ii) Derivatives and optimal rules. Algebraic degree of a Gaussian quadrature rule cannot be improved by adding derivative evaluations of the integrand. This holds for optimal kernel quadrature rules in the sense that derivatives are of no help in minimising the worst-case error (or posterior integral variance). iii) Positivity of the weights. Essentially as a consequence of the preceding property, both the Gaussian and optimal kernel quadrature rules have positive weights (i.e., they are positive linear functionals).
Organizers: Alexandra Gessner
Standard methods of causal discovery take as input a statistical data set of measurements of well-defined causal variables. The goal is then to determine the causal relations among these variables. But how are these causal variables identified or constructed in the first place? Often we have sensor level data but assume that the relevant causal interactions occur at a higher scale of aggregation. Sometimes we only have aggregate measurements of causal interactions at a finer scale. I will motivate the general problem of causal discovery and present recent work on a framework and method for the construction and identification of causal macro-variables that ensures that the resulting causal variables have well-defined intervention distributions. Time permitting, I will show an application of this approach to large scale climate data, for which we were able to identify the macro-phenomenon of El Nino using an unsupervised method on micro-level measurements of the sea surface temperature and wind speeds over the equatorial Pacific.
Organizers: Sebastian Weichwald