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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
_______________________________________________
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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
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…
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Zeit: Donnerstag, 22. Juli 2002, 13.00 Uhr
Ort: Zoom Videokonferenz
https://rwth.zoom.us/j/97475619537?pwd=NzBLYkFqREVISyt3QnNSd1ZoK2NZZz09
Referent: Dipl.-Inf. Joachim Protze
Lehrstuhl Informatik 12
Thema: Modular Techniques and Interfaces for Data Race Detection in
Multi-Paradigm Parallel Programming
Abstract:
The demand for ever-growing computing capabilities in scientific
computing and simulation has led to heterogeneous computing systems with
multiple parallelism levels. The aggregated performance of the Top 500
high-performance computing (HPC) systems showed an annual growth rate of
85% for the years 1993-2013. As this growth rate significantly exceeds
the growth rate of 40% to 60% supported by Moore’s law, the additional
growth was always supported by an increasing number of computing nodes
with distributed memory and connected by a network. The message passing
interface (MPI) proved to be the dominating programming paradigm for
distributed memory computing as the most coarse-grain level of
parallelism in HPC. While performance gain from Moore’s law in the last
century mainly went into single-core performance by increasing the clock
frequency, we see an increasing number of computing cores per socket
since the beginning of this century. The cores within a socket or a
node share the memory. Although MPI can be used and is used for shared
memory parallelization, explicit use of shared memory as with OpenMP can
improve the scalability and performance of parallel applications. As a
result, hybrid MPI and OpenMP programming is a common paradigm in HPC.
Memory access anomalies such as data races are a severe issue in
parallel programming. Data race detection has been studied for years,
and different static and dynamic analysis techniques have been
presented. This work will not try and propose fundamentally new analysis
techniques but will show how high-level abstraction of MPI and OpenMP
can be mapped to the low-level abstraction of analysis tools without
impact on the analysis’s soundness. This work develops and presents
analysis workflows to identify memory access anomalies in hybrid, multi-
paradigm parallel applications. This work collects parallel variants of
memory access anomalies known from sequential programming and identifies
specific patterns for distributed and shared memory programming.
Furthermore, this work identifies the high-level synchronization,
concurrency, and memory access semantics implicitly and explicitly
defined by the parallel programming paradigms’ specifications to provide
a mapping to the analysis abstraction. As part of these high-level
concurrency concepts, we can identify several sources of concurrency
within a thread. This work compares two techniques to handle this high-
level concurrency for data race analysis and finds that a combined
approach works best in the general case. The evaluation shows that this
work’s analysis workflow provides a high precision while enabling
increased recall for concurrency within a thread.
In this talk, we will focus on the mapping of high-level concurrency
abstractions to low-level analysis abstractions as an important key
point of this thesis and present the results of the work.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Zeit: Freitag, 23. Juli 2021, 10:00 Uhr
Zoom: https://rwth.zoom.us/j/97181863376?pwd=VmZIUzlNTXhQRFl0S25uRFBTRW0wdz09
Meeting-ID: 971 8186 3376
Kenncode: 867315
Referent: Richard Wilke, M.Sc.
LuFG Mathematische Grundlagen der Informatik
Thema: Reasoning about Dependence and Independence: Teams and Multiteams
Abstract:
Team semantics is the mathematical basis of modern logics for reasoning about
dependence and independence. Its core feature is that formulae are evaluated
against a set of assignments, called a team. This approach dates back to Hodges (1997)
who used it to provide a compositional semantics for independence friendly logic.
Building on this idea, Väänänen (2007) suggested that dependencies between variables should
not be treated as annotations of quantifiers, but as atomic properties of teams.
However, being based on sets, team semantics can only be used to reason about the
presence or absence of data. Multiteam semantics instead takes multiplicities of
data into account and is based on multisets of assignments, called multiteams.
In this talk we give an overview of this formalism, explore a wide spectrum of logics
with multiteam semantics and compare them with regard to their expressive power.
We exhibit some striking differences between multiteam and team semantics, and also
show where these formalisms are similar. Moreover, we present a game-theoretic
semantics for our logic and establish connections between logics with multiteam
semantics and variants of existential second-order logic.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Zeit: Montag, 19. Juli 2021, 14.30 Uhr
Zoom: https://rwth.zoom.us/j/99446295120?pwd=NWU0QmIvVkpONlV2R0xmbmloTkhZZz09
Referent: Dipl.-Ing. Evgeny Kusmenko
Lehrstuhl Informatik 3
Thema: Model-Driven Development Methodology and Domain-Specific Languages for the Design of Artificial Intelligence in Cyber-Physical Systems
Abstract:
The development of cyber-physical systems poses a multitude of challenges requiring experts from different fields. Such systems cannot be developed successfully without the support of appropriate processes, languages, and tools. Model-driven software engineering is an important approach which helps development teams to cope with the increasing complexity of today's cyber-physical systems. In this talk we are going to discuss a model-driven engineering methodology with a particular focus on interconnected intelligent cyber-physical systems such as cooperative vehicles.
The basis of the proposed methodology is a component-and-connector architecture description language focusing on the decomposition and integration of cyber-physical system software. It features a strong, math-oriented type system abstracting away from
the technical realization and incorporating physical units. To facilitate the development of highly-interconnected self-adaptive systems, the language enables its users to model component and connector arrays and supports architectural runtime-reconfiguration. Architectural elements can be altered, added, and removed dynamically upon the occurrence of trigger events.
In order to fully cover the development process, the proposed methodology, in addition to structural modeling, provides means for behavior specification and its seamless integration into the components of the architecture. A matrix-oriented scripting language enables
the developer to specify algorithms using a syntax close to the mathematical domain. What is more, a dedicated deep learning modeling language is provided for the development and training of neural networks as directed
acyclic graphs of neuron layers. The framework supports different learning methods including supervised, reinforcement, and generative adversarial learning, covering a broad range of applications from image and natural language processing to decision making and test data generation.
The presented toolchain enables an automated generation of fully functional C++ code together with the corresponding build and training scripts based on the architectural models and behavior specifications. Finally, to facilitate the integration and deployment of the modeled software in distributed environments, we use a tagging approach to model the middleware and to control a middleware generation toolchain.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Zeit: Mittwoch, 14. Juli 2021, 10.00 Uhr
Ort: Zoom Videokonferenz
Link: https://rwth.zoom.us/j/94853770165?pwd=Uzk4TjI4TGNJQ3owaXJDbUxMT2d0UT09
Meeting-ID: 948 5377 0165
Kenncode: 046795
Referent: Patrick Landwehr M.Sc.
Lehrstuhl Informatik 7
Thema: Tree Automata with Constraints on Infinite Trees
Abstract:
Tree automata on infinite trees are a powerful tool that is widely used for decision procedures and synthesis of logical specifications.
It is well known that finite tree automata have good algorithmic properties, but somewhat limited expressive power.
For example, they cannot verify that certain subtrees of an input tree are equal.
In order to model such properties, we study extensions of tree automata that use so called constraints to compare whole subtrees of an input.
We distinguish between two types of constraints: local constraints and global constraints.
Local constraints can be used to compare the direct subtrees of each node.
In this thesis we first summarize the existing results of tree automata with local constraints for infinite trees.
Then we paritally answer the open question whether the class of languages recognizable by these automata is closed under projection.
That is, we show that in the case of automata with Büchi acceptance condition the class of recognizable languages is closed under projection.
As a consequence, we obtain a new decision algorithm for the emptiness problem as well as a proof for the fact that each non-empty language recognized by a Büchi tree automaton with sibling constraints contains a regular tree.
Moreover, we also study logical characterizations of this class of languages.
Tree automata with global constraints are able to compare compare subtrees whose positions are defined by the states reached in a run.
For example, this model can verify that all subtrees rooted at positions where a certain state is reached are equal.
In this thesis we generalize the model introduced on finite trees to the setting of infinite trees.
We show that most closure properties and decidability results can be extended from finite to infinite trees.
However, new techniques are required in order to do so.
While the decidability of the emptiness problem remains an open question in general, we present decidability results for some subclasses of tree automata with global constraints.
That is, if the automaton tests only for equality of subtrees (and not for inequality) the emptiness problem is decidable.
The same is true if the underlying language (i.e. when ignoring the constraints) is countable.
We also study the special case of automata with global constraints on unary infinite trees (omega-words).
Here we show that in contrast to branching trees, the class of languages recognizable by these automata is closed under complement.
Finally, we present precise logical characterizations for all of the subclasses mentioned, by extensions of monadic second order logic on infinite trees (or omega-words).
Es laden ein: die Dozentinnen und Dozenten der Informatik
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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