informatik-vortraege December 2020

informatik-vortraege@lists.rwth-aachen.de

4 participants
5 discussions

Einladung Informatik-Oberseminar Malte Nuhn

by Sekretariat I6

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 12. Juli 2019, 10.00 Uhr Ort: Informatikzentrum, E3, Raum 9222 Referent: Dipl.-Inform. Malte Nuhn Thema: Unsupervised Training with Applications in Natural Language Processing// Abstract: The state-of-the-art algorithms for various natural language processing tasks require large amounts of labeled training data. At the same time, obtaining labeled data of high quality is often the most costly step in setting up natural language processing systems.Opposed to this, unlabeled data is much cheaper to obtain and available in larger amounts.Currently, only few training algorithms make use of unlabeled data. In practice, training with only unlabeled data is not performed at all. In this thesis, we study how unlabeled data can be used to train a variety of models used in natural language processing. In particular, we study models applicable to solving substitution ciphers, spelling correction, and machine translation. This thesis lays the groundwork for unsupervised training by presenting and analyzing the corresponding models and unsupervised training problems in a consistent manner.We show that the unsupervised training problem that occurs when breaking one-to-one substitution ciphers is equivalent to the quadratic assignment problem (QAP) if a bigram language model is incorporated and therefore NP-hard. Based on this analysis, we present an effective algorithm for unsupervised training for deterministic substitutions. In the case of English one-to-one substitution ciphers, we show that our novel algorithm achieves results close to human performance, as presented in [Shannon 49]. Also, with this algorithm, we present, to the best of our knowledge, the first automatic decipherment of the second part of the Beale ciphers.Further, for the task of spelling correction, we work out the details of the EM algorithm [Dempster & Laird + 77] and experimentally show that the error rates achieved using purely unsupervised training reach those of supervised training.For handling large vocabularies, we introduce a novel model initialization as well as multiple training procedures that significantly speed up training without hurting the performance of the resulting models significantly.By incorporating an alignment model, we further extend this model such that it can be applied to the task of machine translation. We show that the true lexical and alignment model parameters can be learned without any labeled data: We experimentally show that the corresponding likelihood function attains its maximum for the true model parameters if a sufficient amount of unlabeled data is available. Further, for the problem of spelling correction with symbol substitutions and local swaps, we also show experimentally that the performance achieved with purely unsupervised EM training reaches that of supervised training. Finally, using the methods developed in this thesis, we present results on an unsupervised training task for machine translation with a ten times larger vocabulary than that of tasks investigated in previous work. Es laden ein: die Dozentinnen und Dozenten der Informatik _______________________________________________ -- -- Stephanie Jansen Faculty of Mathematics, Computer Science and Natural Sciences HLTPR - Human Language Technology and Pattern Recognition RWTH Aachen University Ahornstraße 55 D-52074 Aachen Tel. Frau Jansen: +49 241 80-216 06 Tel. Frau Andersen: +49 241 80-216 01 Fax: +49 241 80-22219 sek(a)i6.informatik.rwth-aachen.de www.hltpr.rwth-aachen.de Tel: +49 241 80-216 01/06 Fax: +49 241 80-22219 sek(a)i6.informatik.rwth-aachen.de www.hltpr.rwth-aachen.de

1 year, 3 months

[infprof-l] Einladung Informatik-Oberseminar Philipp Weidel, December 15, 2020

by Abigail Morrison

********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 15. Dezember 2020, 10:00 Uhr Zoom: https://rwth.zoom.us/j/99233095930?pwd=dHhTV253V1ZYUzRtSkk1L3A1REZVUT09 Meeting-ID: 992 3309 5930 Kenncode: 626162 Referent: Philipp Weidel, Dipl. Inform. Thema: Learning and decision making in closed loop simulations of plastic spiking neural networks Abstract: To understand how animals and humans learn, form memories and make decisions is a highly relevant goal both for neuroscience and for fields that take some inspiration from neuroscience, such as machine learning and artificial intelligence. Many models of learning and decision making were developed in the fields of machine learning, artificial intelligence, and computational neuroscience. Although these models aim to describe similar mechanisms, they do not all pursue the same goal. These models can be differentiated between models aiming to reach optimal performance on a specific task (or set of tasks) and models trying to explain how animals and humans learn. Some models of the first class use biologically inspired methods (such as deep learning) but are usually not biologically realistic and are therefore not well suited to explain the function of the brain. Models in the second class focus on being biologically plausible to explain how the brain works, but often demonstrate their capability on too simplistic tasks and yield low performance on well-known tasks from machine learning. This work aims to close the gap between these two types of models. In the first part of this talk, tools are described that allow the combination of biologically plausible neural network models together with powerful toolkits known from machine learning and robotics. To this end, MUSIC, the middleware for spiking neural network simulators such as NEST and NEURON is interfaced with ROS, a middleware for robotic hardware and simulators such as Gazebo. This toolchain is extended with interfaces to reinforcement learning toolkits such as the OpenAI Gym. The second part addresses the question of how the brain can represent its environment in the neural substrate of the cortex and how a realistic model of reinforcement learning can make use of these representations. To this end, a spiking neural network model of unsupervised learning is presented which is able to learn its input projections such that it can detect and represent repeating patterns. By using an actor-critic reinforcement learning architecture driven by a realistic dopamine modulated plasticity rule the model can make use of the representations and learn a range tasks. Es laden ein: die Dozentinnen und Dozenten der Informatik -- Prof. Dr. Abigail Morrison IAS-6 / INM-6 / SimLab Neuroscience Jülich Research Center & Computer Science 3 - Software Engineering RWTH Aachen http://www.fz-juelich.de/inm/inm-6 http://www.fz-juelich.de/ias/jsc/slns http://www.se-rwth.de Office: +49 2461 61-9805 Fax # : +49 2461 61-9460 ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Volker Rieke Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------

2 years, 6 months

Einladung: Informatik-Oberseminar Alexander Hermans

by Alexander Hermans

************************************************************************ * * * Einladung * * * * Informatik-Oberseminar * * * ************************************************************************ Zeit: Montag, 21. Dezember 2020, 13:15-14:15 Uhr Zoom: https://us02web.zoom.us/j/89686061674?pwd=UFUxaWMrdFJtY09JSWRBKzNxUnU0QT09 <https://www.google.com/url?q=https://us02web.zoom.us/j/89686061674?pwd%3DUF…> Meeting-ID: 896 8606 1674 Kenncode: 923078 Referent: Alexander Hermans, M. Sc. Lehrstuhl Informatik 13 Thema: Learning-based Visual Scene and Person Understanding for Mobile Robotics Abstract: We have seen tremendous progress in the computer vision community across the past decades, especially with advancements in deep learning, which is now used as the core machine learning approach for most tasks. However, when deploying computer vision approaches in actual robotic applications, we often find that top-performing methods do not work as well as expected due to hardware constraints and different input data characteristics. We deal with visual scene and person understanding which are highly relevant for robotics applications. Robots need to be able to understand their environment and take special care around persons to ensure a safe navigation and interaction. We specifically deal with three important sub-tasks: semantic segmentation, 2D laser-based object detection, and person re-identification. Semantic segmentation deals with the task of labeling every pixel or point in a scene with a class label. This can in turn be used to extract higher level information about the surrounding scene, which can be used as context for further planning and interaction tasks. While the resulting segmentations provide object labels, they do not contain instance labels, making it hard to detect object instances. However, object detection is an important capability for allowing robots to safely navigate between dynamic objects. Especially the detection of persons is an important task, enabling robots to interact with us. Since many mobile platforms are already equipped with a 2D laser scanner, they are interesting input sensors for object detection, even though the resulting scans only contain sparse data. In addition to person detection, person re-identification is an important task. This can be used to improve tracking, but also allows to gather longer-term statistics and enables person specific interactions. For each of the three tasks we propose state-of-the-art approaches, however, we also consider aspects that are important for their real-world deployability and show applications of our methods within the context of robotics projects. Es laden ein: die Dozentinnen und Dozenten der Informatik

2 years, 9 months

Einladung: Informatik-Oberseminar Frau Can Sun

by Leany Maaßen

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 17. Dezember 2020, 15:00-16:00 Uhr Zoom: https://rwth.zoom.us/j/92609933435?pwd=OStsV2w0UUZrK2NmNnE4WDE0Y0pEQT09 Meeting-ID: 926 0993 3435 Kenncode: 584723 Referentin: Frau Can Sun, M. Sc. Lehrstuhl Informatik 5 Thema: Embedding of real-world complexity modeling in adaptive supply chain systems engineering Abstract: Successful supply chain complexity and change management is critical for companies to gain competitive advantages. This thesis aims to build a comprehensive supply chain complexity measurement framework through detailed modeling and apply it in the changeable environment. The research starts with modeling the supply chain and its dynamic change as sociotechnical systems. Then a complexity measurement framework that is decomposed into different levels of complexity drivers is developed, and a decision-making framework for various change scenarios is proposed by highlighting complexity as one of the decisive evaluation criteria. The proposed solutions are validated with a set of real-world case studies from the semiconductor industry. Each case represents one change scenario and addresses the complexity from a particular perspective; the problem is analyzed and modeled with the support of proposed models and frameworks. The results show that complexity can be measured by a set of indicators or formulas, and thus support decision-making by comparing the change-induced complexity. Es laden ein: die Dozentinnen und Dozenten der Informatik _______________________________ Leany Maaßen RWTH Aachen University Lehrstuhl Informatik 5, LuFG Informatik 5 Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke, Prof. Gerhard Lakemeyer Ph.D. Ahornstrasse 55 D-52074 Aachen Tel: 0241-80-21509 Fax: 0241-80-22321 E-Mail: maassen(a)dbis.rwth-aachen.de

2 years, 9 months

reminder: today, 10:30, Kuldeep Meel (NUS Singapore): Sparse Hashing for Scalable Approximate Model Counting: When Theory and Practice Finally Meet

by Bolke-Hermanns, Helen

Dear all, This is a short reminder about the talk of Kuldeep Meel (NUS Singapore)<https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Das-Graduiertenkolleg/Gastwi…> today at 10:30. Title: Sparse Hashing for Scalable Approximate Model Counting: When Theory and Practice Finally Meet Given a Boolean formula F, the problem of model counting, also referred to as #SAT, is to compute the number of solutions of F. The hashing-based techniques for approximatecounting have emerged as a dominant approach, promising achievement of both scalability and rigorous theoretical guarantees. The standard construction of strongly 2-universal hash functions employs dense XORs (i.e., involving half of the variables in expectation), which is widely known to cause degradation in the runtime performance of state of the art SAT solvers. Consequently, the past few years have witnessed an intense activity in the design of sparse XORs as hash functions. In this talk, we will first survey the known results, and identify the crucial bottleneck contributing to their lack of ability to provide speedups. We will then formalize a relaxation of universal hashing, called concentrated hashing, and establish a novel and beautiful connection between concentration measures of these hash functions and isoperimetric inequalities on boolean hypercubes. This allows us to obtain tight bounds on variance as well as the dispersion index and show that logarithmically sized XORs suffices for the design of sparse hash functions belonging concentrated hash family. Finally, we use sparse hash functions belonging to this concentrated hash family to develop new approximate counting algorithms. Our comprehensive experimental evaluation of our algorithm on 1896 benchmarks with computational effort of over 20,000 computational hours demonstrates speedup compared to existing approaches. To the best of our knowledge, this work is the first study to demonstrate runtime improvement of approximate model counting algorithms through the usage of sparse hash functions, while still retaining strong theoretical guarantees. (Based on Joint work with S. Akshay, D. Agarwal, and Bhavishya; The corresponding papers were published at SAT-20 and LICS-20) Bio: Kuldeep Meel is Sung Kah Kay Assistant Professor of Computer Science in School of Computing at the National University of Singapore. He received his Ph.D. (2017) and M.S. (2014) degree from Rice University, and B. Tech. (with Honors) degree (2012) in Computer Science and Engineering from Indian Institute of Technology, Bombay. He is a recipient of 2019 NRF Fellowship for AI. His research interests lie at the intersection of Artificial Intelligence and Formal Methods. His work received the 2018 Ralph Budd Award for Best PhD Thesis in Engineering, 2014 Outstanding Masters Thesis Award from Vienna Center of Logic and Algorithms and Best Student Paper Award at CP 201 Wednesday, 2.12.2020, 10:30 https://rwth.zoom.us/j/92047949381?pwd=LzIwUW96WEM0MkRjZ01FUmhwd1I3QT09<https://www.google.com/url?q=https://rwth.zoom.us/j/92047949381?pwd%3DLzIwU…> Meeting ID: 920 4794 9381 Password: unravel https://www.unravel.rwth-aachen.de/go/id/kfsgc Best regards Helen Bolke-Hermanns Helen Bolke-Hermanns RTG UnRAVeL - RWTH Aachen University Ahornstr. 55, D-52074 Aachen Building E3, 2nd floor, Room 9218 Telefon: +49 (241) 80-21 004 Fax: +49 (241) 80-22 215 E-Mail: Helen.Bolke-Hermanns(a)Informatik.RWTH-Aachen.de<mailto:Helen.Bolke-Hermanns@Informatik.RWTH-Aachen.de> Web: www.unravel.rwth-aachen.de<http://www.unravel.rwth-aachen.de/> [rwth_unravel_en_rgb]

2 years, 9 months

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informatik-vortraege December 2020