March 2022 - informatik-vortraege - lists.rwth-aachen.de

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 Krishna Subramanian
by Subramanian, Krishna 06 Apr '22

06 Apr '22

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 8. April 2022, 12.15 Uhr Zoom URL: https://rwth.zoom.us/j/97644054920 Referent: Krishna Subramanian, M.Sc. Lehrstuhl für Informatik 10 Thema: Lowering the Barriers to Hypothesis-Driven Data Science Abstract: Data science is a frequent task in academia and industry. One common use of data science is to validate hypotheses, in which the analyst uses significance-based hypothesis testing to draw insights about a population distribution based on experimental data. Apart from data scientists, who are professionally trained in data science and are highly skilled, many non-professional analysts also carry out data analysis. These non-professionals, who we refer to as data workers, are domain experts who lack expertise in data science, such as academic researchers, project managers, and sales managers. Through interviews, observations, online surveys, and content analyses, we aim to understand data workers' workflows across important tasks in hypothesis testing: learning theoretical and practical statistics, selecting statistical procedures, using data science programming IDEs to experiment with ideas in source code, refine and refactor source code, and disseminating findings from an analysis.  We present our findings grouped into two steps when performing data science tasks: 1. Preparing to perform data science tasks: We discuss our findings about the impact of formal training on real-world statistical practice; trade-offs among information sources used for selecting statistical procedures; perceived complexity and uncertainty about statistical procedure selection; and reluctance among data workers to adopt alternative methods of analysis.  Based on the above findings, we present design recommendations and two artifacts to improve data workers' workflows. Our artifacts include Statsplorer, a web-based tool to help data workers kickstart analysis and learn about common issues in statistical practice, such as over-testing, overlooking assumptions, and selecting the appropriate test; and StatPlayground, an interactive simulation tool that can be used to self-learn or teach statistical concepts and statistical procedure selection.  2. Performing data science tasks: Our findings include an overview of data workers' workflows when performing hypothesis testing using programming IDEs, which follows an exploratory programming workflow; and a comparison of existing interfaces for data science programming, namely computational notebooks, scripts, and consoles, and a discussion of how well they support various steps in hypothesis testing.  To improve data workers' workflows when performing data science tasks, we contribute design recommendations and two artifacts. Our artifacts include StatWire, an experimental hybrid-programming interface that encourages data workers to write high-quality source code; and Tractus, an interactive visualization that can lower the cost of working with experimental source code. Based on our work, we present four takeaways that can be used by researchers, software developers, and educators to lower the barriers to hypothesis testing. --- Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Janis Born
by Janis Born 08 Mar '22

08 Mar '22

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 29. März 2022, 15:00 Uhr Ort: https://rwth.zoom.us/j/95327979988?pwd=VU8rT1oyVGhiZENvQ2NuVVB2UVVndz09 Referent: Janis Born M.Sc. Lehrstuhl für Informatik 8 Thema: Topological Aspects of Maps Between Surfaces Abstract: We consider the generation of high-quality maps between 3D surfaces in the form of discrete homeomorphisms. Specifically, we address the topological issues underlying the construction of such maps, which have so far received comparably little attention in geometry processing research. We approach this task from two different angles: First, we propose a robust method for the construction of maps from sparse landmark correspondences, based on compatible layout embeddings. Our robust embedding strategy systematically searches for short, natural embeddings and therefore reliably avoids a range of sporadic topological initialization errors which can occur with previous heuristic approaches. Second, we introduce a novel algorithm to extract topological map descriptions from approximate, non-homeomorphic input maps. Such a purely abstract description of map topology may then be used to guide the construction of a proper homeomorphism. As our inference method is highly robust to a wide range of map defects and imperfect map representations, this effectively allows to delegate the difficult task of finding a natural map topology to specialized shape matching methods, which have grown increasingly capable. These advancements promote the further automation of map generation techniques in two regards: They vastly reduce the need for human supervision, and make the results of automatic shape matching methods accessible for topological initialization. Es laden ein: die Dozentinnen und Dozenten der Informatik

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