January 2020 - 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 zum Informatik-Oberseminar, "Feature-aware and feature-driven editing of 3D surface meshes", Referentin Ellen Dekkers
by Ellen Dekkers 20 Jan '20

20 Jan '20

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 11. Februar 2020, 14.00 Uhr Ort: Ahornstraße 55, E3, Raum 118 (Seminar-Raum I8) Referentin: Dipl.-Inform. Ellen Dekkers Lehrstuhl Informatik 8 Thema: Feature-aware and feature-driven editing of 3D surface meshes Abstract: Feature-aware and feature-driven editing of three-dimensional surface meshes is a very prominent task in Computer Graphics and Geometry Processing applications. Existing methods can be roughly classified into general elastic approaches, which aim at the preservation of a manifold surface's differential properties and hence of local, low-level geometric surface detail, and structure-aware editing techniques focusing on the preservation of high-level surface structures such as feature curves or regular patterns. In this talk, we review both research fields and discuss the respective approaches with a particular focus on their capabilities in preserving various types of surface features. We then present a novel approach to feature-aware mesh editing that combines elastic Laplacian deformation with discrete plastic topology modifications by transferring the concept of seam carving from the image retargeting to the mesh deformation scenario. During editing, a precomputed set of triangle strips, or geometry seams, can be dynamically deleted or inserted in low saliency mesh regions, thereby distributing the deformation distortion non-homogeneously over the model which yields a much better preservation of salient surface features compared to standard elastic deformation. Finally, we remove the manifold restriction and address feature curve driven editing of non-manifold meshes. First, we propose a semi-automatic approach to efficiently and robustly recover characteristic feature curves from free-form surfaces. We then present two practical applications of this technique, the first of which exploits the curves' shape-defining properties and employs them as intuitive modeling handles for editing non-manifold surfaces. In our second application, we turn to a practical scenario in reverse engineering and consider the problem of generating a statistical shape model for car bodies. The crucial step of establishing proper feature correspondences between a large number of input models that exhibit significant shape variations is essentially guided by characteristic feature curves. These curves furthermore serve as modeling metaphors for intuitive exploration of the shape space spanned by the input models, thereby enabling the generation of semantically meaningful, novel car bodies. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik Oberseminar Sebastian Junges
by Junges, Sebastian 15 Jan '20

15 Jan '20

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 13. Februar 2019, 13.00 Uhr Ort: Informatikzentrum, E3, Raum 9222 Referent: Sebastian Junges, M.Sc. Lehrstuhl für Informatik 2 (Software Modeling and Verification) Thema: Parameter Synthesis in Markov Models Abstract: Markov models comprise states with probabilistic transitions. The analysis of these models is ubiquitous and studied in, among others, reliability engineering, artificial intelligence, systems biology, and formal methods. Naturally, their analysis crucially depends on the transition probabilities. Often, these probabilities are approximations based on data or reflect configurable parts of a modelled system. To represent the uncertainty about the probabilities, we study parametric Markov models, in which the probabilities are symbolic expressions rather than concrete values. More precisely, we consider parametric Markov decision processes (pMDPs) and parametric Markov chains (pMCs) as special case. Substitution of the parameters yields classical, parameter-free Markov decision processes (MDPs) and Markov chains (MCs). A pMDP thus induces uncountably many MDPs. Each MDP may satisfy reachability and reward properties, such as "the maximal probability that the system reaches an `offline' state is less than 0.01%", or "the maximal expected energy consumption is less than 20 kWh." Lifting these properties to pMDPs yields fundamental problems asking: - "Is there an induced MDP satisfying the property?" (feasibility), its dual - "Do all induced MDPs satisfy the property?" (validity), and advanced problems such as "What is a concise representation for all induced MCs satisfying the property?" We study these problems on a conceptual level, and design and implement both improved and novel algorithms. On the conceptual side, a thorough discussion of the feasibility problem yields new results, such as: (1) that answering various variants of the feasibility problem is — in terms of complexity — as hard as finding roots of a multivariate polynomial, and (2) that these problems are tightly connected to the analysis of memoryless strategies in partially observable MDPs, a famous model in artificial intelligence. Additionally, we introduce family MCs (fMCs), a subclass of pMCs with finitely many induced MCs. Among others, fMCs define fundamental problems underlying the quantitative analysis of software product lines and sketching of probabilistic programs. On the algorithmic side, (1) we present and analyse improved but previously known approaches, and combine them to meet practical needs. Their analysis inspired (2) three new and orthogonal approaches, utilising advances in convex optimisation, as well as adapting prominent ideas such as inductive synthesis and abstraction-refinement to the particular setting. All methods efficiently exploit advances in the off-the-shelf analysis of MDPs. On the empirical side, the new methods improve the state-of-the-art considerably, handling hundreds of parameters and millions of states. All the approaches we present have been implemented in open-source tools. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Vortrag Dr. Markus Freitag (heute)
by Christian Herold 14 Jan '20

14 Jan '20

Sehr geehrte Damen und Herren, wir möchten Sie freundlich auf den Vortrag (s.u.) von Herr Dr. Markus Freitag heute Nachmittag um 14:30 hinweisen. Bitte entschuldigen Sie die kurzfristige Ankündigung. Mit freundlichen Grüßen, Christian Herold *********************************************************************** * * * Einladung zum Gastvortrag * * * ************************************************************************ Zeit: Dienstag, 14. Januar 2020, 14:30 Ort: Ahornstraße 55, E2, Raum 5056 Referent: Dr. Markus Freitag Titel: (Some) Research Happening at Google Translate Abstract: Machine Translation is one of the most appealing research topics in Natural Language Processing and Machine Learning. In this talk, you will be given an overview of some of the current research efforts happening at Google Translate. We will start with a project with the end-goal of training a single model to translate between all languages supported by Google Translate. Neural models can be trained to perform several tasks simultaneously as exemplified by multilingual NMT using a single model to translate between multiple languages. Apart from reducing operational costs, multilingual models improve performance on low and zero-resource language pairs due to joint training. We attempt to study multilingual neural machine translation, using a massive open-domain dataset containing over 25 billion parallel sentences in 103 languages. In the second half of the talk, we focus on translationese, a term that refers to artifacts present in text that was translated into a given language that distinguish it from text originally written in that language. These artifacts include lexical and word order choices that are influenced by the source language as well as the use of more explicit and simpler constructions. Machine translation has an undesirable propensity to produce translationese artifacts, which can lead to higher BLEU scores while being liked less by human raters. First, we train an Automatic Post Editing (APE) model that convert the translationese output into a more natural text. We use this model as a tool to reveal systematic problems with reference translations. Second, we model translationese and original (i.e. natural) text as separate languages in a multilingual model, and pose the question: can we perform zero-shot translation between original source text and original target text? To sum up, we will discuss why a loss in BLEU score does not always mean lower translation quality. -- Christian Herold Faculty of Mathematics, Computer Science and Natural Sciences HLTPR - Human Language Technology and Pattern Recognition RWTH Aachen University Ahornstraße 55 D-52074 Aachen Tel: +49 241 80 21613 Fax: +49 241 80 22219 herold(a)i6.informatik.rwth-aachen.de www.hltpr.rwth-aachen.de

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Erinnerung: Informatik-Kolloquium Michael Schaub (08.01.2020 **morgen**, 14:00)
by Joost-Pieter Katoen 07 Jan '20

07 Jan '20

*********************************************************************** * * * Einladung * * * Informatik-Kolloquium * * *********************************************************************** Zeit: Mittwoch, 8. Januar 2020, 14:00 Uhr Ort: Raum 9222, Gebäude E3, Informatikzentrum Referent: Michael Schaub University of Oxford, UK and MIT, USA Thema: Data Science for Networks Abstract: Networks have become a widely adopted model for a range of systems, cutting across Science and Engineering. However, our theoretical understanding of many fundamental phenomena that arise in complex networks and networked systems is still limited. My vision is to develop a data science for networks and dynamical systems that will contribute to addressing this challenge, by combining data-driven and model-based approaches, using the language of graphs and networks. In this talk, we will give a brief overview of such a Data Science for Networks. We first discuss how networks appear naturally within models in different research domains and illustrate the underlying scientific questions via examples drawn from applications. We then examine in some detail the problem of feature learning from graphs with unobserved edges, in which we aim to learn certain aspects of a graph solely from dynamical observations on its nodes, without knowledge of the edge-set of the graph. We conclude with a brief outlook on future challenges and open problems. Es laden ein: die Dozentinnen und Dozenten der Informatik

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