informatik-vortraege März 2021

informatik-vortraege@lists.rwth-aachen.de

2 Teilnehmer
4 Diskussionen

Einladung Informatik-Oberseminar Malte Nuhn

von 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 Jahr, 10 Monate

UnRAVeL "Behind the Scenes" Survey Lecture

von Tim Seppelt

Dear all, part of the programme of the research training group UnRAVeL is a series of introductory lectures on the topics of „randomness“ and „uncertainty“ in UnRAVeL’s research thrusts algorithms and complexity, verification, logic and languages, and their application scenarios. Each lecture is delivered by one of the researchers involved in UnRAVeL. The main aim is to provide doctoral researchers as well as master students a broad overview of the subjects of UnRAVeL. This year, 12 UnRAVeL professors will answer the following questions, based on one of their recent scientific results: * How did you get to this result? * How did you come up with certain key ideas? * How did you cope with obstacles on the way? Which ideas you had did not work out? Following these talks, PhD students will give an informal summary of their doctoral studies within UnRAVeL. All interested doctoral researchers and master students are invited to attend the UnRAVeL lecture series 2021 and engage in discussions with researchers and doctoral students. Details information can be found on https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-… All events take place on *Thursdays from 16:30 to 18:00 on Zoom* https://rwth.zoom.us/j/96043715437?pwd=U0dRczkyQjRCY21abW13TDNmUHlhUT09 * 15/04/2021 Survey Lecture: Erika Ábrahám: Probabilistic Hyperproperties * 22/04/2021 Jürgen Giesl: Inferring Expected Runtimes of Probabilistic Programs * 29/04/2021 Erich Grädel: Hidden Variables in Quantum Mechanics and Logics of Dependence and Independence * 06/05/2021 Christof Löding: Learning Automata for Infinite Words * 20/05/2021 Martin Grohe: The Logic of Graph Neural Networks * 10/06/2021 Britta Peis: Sensitivity Analysis for Submodular Function Optimization with Applications in Algorithmic Game Theory * 17/06/2021 Nils Nießen: Optimised Maintenance of Railway Infrastructure * 24/06/2021 Gerhard Lakemeyer: Uncertainty in Robotics * 01/07/2021 Joost-Pieter Katoen: The Surprises of Probabilistic Termination * 08/07/2021 Christina Büsing: Robust Minimum Cost Flow Problem Under Consistent Flow Constraints * 15/07/2021 Ringvorlesung: Gerhard Woeginger: Bilevel optimization * 22/07/2021 Ulrike Meyer: Malware Detection We are looking forward to seeing you at the lectures. Best regards, Tim Seppelt for the organisation committee https://www.unravel.rwth-aachen.de/global/show_picture.asp?id=aaaaaaaaaydoc…

2 Jahre, 8 Monate

Einladung: Informatik-Oberseminar Andrea Schnorr

von Andrea Schnorr

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 19. Februar 2021, 11.00 Uhr Zoom: https://rwth.zoom.us/j/2452218628 Referent: Andrea Schnorr, M.Sc. LuFG i12 Thema: Feature Tracking for Space-Filling Structures Abstract: Feature-based visualization is a proven strategy to deal with the massive amounts of data emerging from time-dependent simulations: the analysis focuses on meaningful structures, i.e., said features. Feature tracking algorithms aim at automatically finding corresponding objects in successive time steps of these time-dependent data sets in order to assemble the individual objects into spatio-temporal features. Classically, feature-based visualization has focused on sparse structures, i.e. structures which cover only a small portion of the data domain. Given a sufficiently high temporal resolution, existing tracking approaches are able to reliably resolve the correspondence between feature objects of successive time steps. Our research is motivated by our collaborators' work on the statistical analysis of structures that are space-filling by definition: dissipation elements. Space-filling structures partition the entire domain. Our collaborators aim at extending their statistical analysis to a time-dependent setting. Hence, we introduce an efficient approach for general feature tracking which handles both sparse and space-filling data. To this end, we develop a framework for automatic evaluation of tracking approaches, an algorithmic framework for feature tracking, and an efficient implementation of this framework. First, we propose a novel evaluation framework based on algorithmic data generators, which provide synthetic data sets and the corresponding ground truth data. This framework facilitates the structured quantitative analysis of an approach's feature tracking performance and the comparison of different approaches based on the resulting measurements. Second, we introduce a novel approach for tracking both sparse and space-filling features. The correspondence between neighboring time-steps is determined by successively solving two graph optimization problems. In the first phase, one-to-one assignments are resolved by computing a maximum-weight, maximum-cardinality matching on a bi-partite graph. In its second phase, the algorithm detects events by finding a maximum weight independent set in a graph of all possible, potentially conflicting event explanations. Third, we show an optimized version of the second stage of the tracking framework which exploits the model-specific graph structure arising for the tracking problem. The method's effectiveness is demonstrated by a set of case studies including the use of the evaluation framework as well as the analysis of miscellaneous real-world simulation data sets. Es laden ein: die Dozentinnen und Dozenten der Informatik

2 Jahre, 9 Monate

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

von 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 ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------

3 Jahre, 1 Monat

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informatik-vortraege März 2021