@inproceedings{1098, abstract = {Better understanding of the potential benefits of information transfer and representation learning is an important step towards the goal of building intelligent systems that are able to persist in the world and learn over time. In this work, we consider a setting where the learner encounters a stream of tasks but is able to retain only limited information from each encountered task, such as a learned predictor. In contrast to most previous works analyzing this scenario, we do not make any distributional assumptions on the task generating process. Instead, we formulate a complexity measure that captures the diversity of the observed tasks. We provide a lifelong learning algorithm with error guarantees for every observed task (rather than on average). We show sample complexity reductions in comparison to solving every task in isolation in terms of our task complexity measure. Further, our algorithmic framework can naturally be viewed as learning a representation from encountered tasks with a neural network.}, author = {Pentina, Anastasia and Urner, Ruth}, location = {Barcelona, Spain}, pages = {3619--3627}, publisher = {Neural Information Processing Systems}, title = {{Lifelong learning with weighted majority votes}}, volume = {29}, year = {2016}, } @inproceedings{1102, abstract = {Weakly-supervised object localization methods tend to fail for object classes that consistently co-occur with the same background elements, e.g. trains on tracks. We propose a method to overcome these failures by adding a very small amount of model-specific additional annotation. The main idea is to cluster a deep network\'s mid-level representations and assign object or distractor labels to each cluster. Experiments show substantially improved localization results on the challenging ILSVC2014 dataset for bounding box detection and the PASCAL VOC2012 dataset for semantic segmentation.}, author = {Kolesnikov, Alexander and Lampert, Christoph}, booktitle = {Proceedings of the British Machine Vision Conference 2016}, location = {York, United Kingdom}, pages = {92.1--92.12}, publisher = {BMVA Press}, title = {{Improving weakly-supervised object localization by micro-annotation}}, doi = {10.5244/C.30.92}, volume = {2016-September}, year = {2016}, } @inproceedings{1214, abstract = {With the accelerated development of robot technologies, optimal control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of the history of sensor values, guided by the goals, intentions, objectives, learning schemes, and so forth. While very successful with classical robots, these methods run into severe difficulties when applied to soft robots, a new field of robotics with large interest for human-robot interaction. We claim that a novel controller paradigm opens new perspective for this field. This paper applies a recently developed neuro controller with differential extrinsic synaptic plasticity to a muscle-tendon driven arm-shoulder system from the Myorobotics toolkit. In the experiments, we observe a vast variety of self-organized behavior patterns: when left alone, the arm realizes pseudo-random sequences of different poses. By applying physical forces, the system can be entrained into definite motion patterns like wiping a table. Most interestingly, after attaching an object, the controller gets in a functional resonance with the object's internal dynamics, starting to shake spontaneously bottles half-filled with water or sensitively driving an attached pendulum into a circular mode. When attached to the crank of a wheel the neural system independently develops to rotate it. In this way, the robot discovers affordances of objects its body is interacting with.}, author = {Martius, Georg S and Hostettler, Raphael and Knoll, Alois and Der, Ralf}, location = {Daejeon, Korea}, publisher = {IEEE}, title = {{Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm}}, doi = {10.1109/IROS.2016.7759138}, volume = {2016-November}, year = {2016}, } @inproceedings{1369, abstract = {We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to expand objects based on the information about which classes can occur in an image, and to constrain the segmentations to coincide with object boundaries. We show experimentally that training a deep convolutional neural network using the proposed loss function leads to substantially better segmentations than previous state-of-the-art methods on the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the working mechanism of our method by a detailed experimental study that illustrates how the segmentation quality is affected by each term of the proposed loss function as well as their combinations.}, author = {Kolesnikov, Alexander and Lampert, Christoph}, location = {Amsterdam, The Netherlands}, pages = {695 -- 711}, publisher = {Springer}, title = {{Seed, expand and constrain: Three principles for weakly-supervised image segmentation}}, doi = {10.1007/978-3-319-46493-0_42}, volume = {9908}, year = {2016}, } @inproceedings{1707, abstract = {Volunteer supporters play an important role in modern crisis and disaster management. In the times of mobile Internet devices, help from thousands of volunteers can be requested within a short time span, thus relieving professional helpers from minor chores or geographically spread-out tasks. However, the simultaneous availability of many volunteers also poses new problems. In particular, the volunteer efforts must be well coordinated, or otherwise situations might emerge in which too many idle volunteers at one location become more of a burden than a relief to the professionals. In this work, we study the task of optimally assigning volunteers to selected locations, e.g. in order to perform regular measurements, to report on damage, or to distribute information or resources to the population in a crisis situation. We formulate the assignment tasks as an optimization problem and propose an effective and efficient solution procedure. Experiments on real data of the Team Österreich, consisting of over 36,000 Austrian volunteers, show the effectiveness and efficiency of our approach.}, author = {Pielorz, Jasmin and Lampert, Christoph}, location = {Rennes, France}, publisher = {IEEE}, title = {{Optimal geospatial allocation of volunteers for crisis management}}, doi = {10.1109/ICT-DM.2015.7402041}, year = {2016}, }