February 2022 - informatik-vortraege

Einladung Informatik-Oberseminar Malte Nuhn
by Sekretariat I6 30 May '22

30 May '22

+********************************************************************** * * * 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

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Einladung Informatik-Oberseminar: Linnea: A Compiler for Mapping
by Barthels, Henrik 21 Feb '22

21 Feb '22

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 23. März 2022, 15:00 Uhr Ort: Online (Zoom: https://umu.zoom.us/my/pauldj) Referent: Henrik Barthels, M.Sc. High-Performance and Automatic Computing Group, AICES. Thema: Linnea: A Compiler for Mapping Linear Algebra Problems onto High-Performance Kernel Libraries Abstract: The translation of linear algebra computations into efficient sequences of library calls is a non-trivial task that requires expertise in both linear algebra and high-performance computing. Almost all high-level languages and libraries for matrix computations (e.g., Matlab, Eigen) internally use optimized kernels such as those provided by BLAS and LAPACK; however, their translation algorithms are often too simplistic and thus lead to a suboptimal use of said kernels, resulting in significant performance losses. In order to combine the productivity offered by high-level languages, and the performance of low-level kernels, we are developing Linnea, a code generator for linear algebra problems. As input, Linnea takes a high-level description of a linear algebra problem; as output, it returns an efficient sequence of calls to high-performance kernels. Linnea uses a custom best-first search algorithm to find a first solution in less than a second, and increasingly better solutions when given more time. In 125 test problems, the code generated by Linnea almost always outperforms Matlab, Julia, Eigen and Armadillo, with speedups up to and exceeding 10x. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Re: [lufgi9-mitarbeiter] [assis-i9] [Informatik-assistenten]
by Tim Seppelt 15 Feb '22

15 Feb '22

Regards! Greetings! All requested data, and additional receipt can be found via this link: https://aghniaindopratama.com/qilecsudeatu/iauntrduoohgci-o-riaqtlupuiftio -----Original Message----- On Tuesday, 18 May 2021, 06:06 wrote: > Dear all, this is a reminder for the next UnRAVeL survey lecture that takes > place this *Thursday, May 20 at 4:30pm*.*Martin Grohe *will talk about *The > Logic of Graph Neural Networks*. Following the talk, UnRAVeL PhD student > *Tim Seppelt* will give an informal summary of their doctoral studies > within UnRAVeL. > *Abstract* > Graph neural networks (GNNs) are a deep > learning architecture for > graph structured data that has developed into a > method of choice for > many graph learning problems in recent years. It is > therefore > important that we understand their power. One aspect of this is > the > expressiveness: which functions on graphs can be expressed by a GNN > > model? Surprisingly, this question has a precise answer in terms of > logic > and a combinatorial algorithm known as the Weisfeiler–Leman > algorithm. > >> In my lecture, I will introduce the basic GNN architecture and also > > some extensions, and I will explain the logical characterisations of > > their expressiveness. Further information can be found on > https://www.unravel.rwth-aachen.de/go/id/mxjrr?lidx=1#aaaaaaaaaamxjvb and > below. The event takes place on Zoom: > https://rwth.zoom.us/j/96043715437?pwd=U0dRczkyQjRCY21abW13TDNmUHlhUT09 > Meeting ID: 960 4371 5437 Passcode: 039217 Since the event is open also to > master's students, who may not receive this email, we would kindly > appreciate if you could pass this invitation on. We are looking forward to > seeing many of you at the survey lecture. Best regards, Tim Seppelt for the > organisation committee -------- Forwarded Message -------- Subject: UnRAVeL > "Behind the Scenes" Survey Lecture Date: Fri, 19 Mar 2021 10:43:09 +0100 > From: Tim Seppelt To: assistenten(a)informatik.rwth-aachen.de, > vortraege(a)informatik.rwth-aachen.de CC: Andreas Klinger , Birgit Willms , > Dennis Fischer 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 * > 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 Gerhard > Woeginger: Bilevel optimization /(to be rescheduled)/ * 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…

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Einladung: Informatik-Oberseminar Herr Vinoth Sermuga Pandian
by Leany Maaßen 02 Feb '22

02 Feb '22

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 15.02.2022, 13:00-14:00 Uhr Zoom: <https://rwth.zoom.us/j/95759728127?pwd=RFhzRTh1STJYTXZyanVIdWYweVkwZz09> https://rwth.zoom.us/j/95759728127?pwd=RFhzRTh1STJYTXZyanVIdWYweVkwZz09 Meeting-ID: 957 5972 8127 Kenncode: 112136 Referent: Herr Vinoth Sermuga Pandian, M.Sc. Lehrstuhl Informatik 5 Thema: BlackBox Toolkit: Intelligent Assistance to UI Design Abstract: This dissertation conducts systematic research using a human-centred approach to provide Artificial Intelligence (AI) assistance to User Interface (UI) designers before, during, and after the traditional low fidelity (LoFi) prototyping process. As a result, it aims to provide coherent AI assistance throughout the repetitive and arduous LoFi prototyping task without sacrificing the autonomy of UI designers. In doing so, we contribute the BlackBox Toolkit. This toolkit assists designers by creating four large-scale, diverse, open-access benchmark datasets and three AI tools that assist UI designers throughout the LoFi prototyping process. The quantitative and qualitative evaluation of the AI tools shows that the UI designers perceive utilising AI for UI design as a novel and helpful approach and express their willingness to adopt it. The After Scenario Questionnaire study to measure designer satisfaction results show an above-average satisfaction level for all three AI assistance tools. This research aims to understand the impact of AI tools in UI designer workflow and assess their satisfaction upon using these AI tools. Further, it sets a baseline for future research on UI wireframe generation, refinement and transformation. 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

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