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Events for May 07, 2018

  • Repeating EventSix Sigma Green Belt for Process Improvement

    Mon, May 07, 2018

    Executive Education

    Conferences, Lectures, & Seminars


    Abstract: Learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Master the use of Six Sigma to quantify the critical quality issues in your company. Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure. Six Sigma methods increase productivity and enhance quality.

    More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-green-belt-process-improvement/

    Audiences: Registered Attendees

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    Posted By: Corporate & Professional Programs

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  • PhD Defense - Marjan Ghazvininejad

    Mon, May 07, 2018 @ 10:00 AM - 12:00 PM

    Computer Science

    University Calendar


    PhD Candidate: Marjan Ghazvininejad

    Committee: Kevin Knight (chair), Morteza Dehghani, Jonathan May

    Title: Neural Creative Language Generation

    Time & place: Monday, May 7th, 10 am, GFS 204


    Abstract: Natural language generation (NLG) is a well-studied and still very challenging field in natural language processing. One of the less studied NLG tasks is the generation of creative texts such as jokes, puns, or poems. Multiple reasons contribute to the difficulty of research in this area. First, no immediate application exists for creative language generation. This has made the research on creative NLG extremely diverse, having different goals, assumptions, and constraints. Second, no quantitative measure exists for creative NLG tasks. Consequently, it is often difficult to tune the parameters of creative generation models and drive improvements to these systems. Lack of a quantitative metric and the absence of a well-defined immediate application makes comparing different methods and finding the state of the art an almost impossible task in this area. Finally, rule-based systems for creative language generation are not yet combined with deep learning methods. Rule-based systems are powerful in capturing human knowledge, but it is often too time-consuming to present all the required knowledge in rules. On the other hand, deep learning models can automatically extract knowledge from the data, but they often miss out some essential knowledge that can be easily captured in rule-based systems.
    In this work, we address these challenges for poetry generation, which is one of the main areas of creative language generation. We introduce password poems as a new application for poetry generation. These passwords are highly secure, and we show that they are easier to recall and preferable compared to passwords created by other methods that guarantee the same level of security. Furthermore, we combine finite-state machinery with deep learning models in a system for generating poems for any given topic. We introduce a quantitative metric for evaluating the generated poems and build the first interactive poetry generation system that enables users to revise system generated poems by adjusting style configuration settings like alliteration, concreteness and the sentiment of the poem. The system interface also allows users to rate the quality of the poem. We collect users' rating for poems with various style settings and use them to automatically tune the system style parameters. In order to improve the coherence of generated poems, we introduce a method to borrow ideas from existing human literature and build a poetry translation system. We study how poetry translation is different from translation of non-creative texts by measuring the language variation added during the translation process. We show that humans translate poems much more freely compared to general texts. Based on this observation, we build a machine translation system specifically for translating poetry which uses language variation in the translation process to generate rhythmic and rhyming translations.

    Location: 204

    Audiences: Everyone Is Invited

    Posted By: Lizsl De Leon

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  • PhD Defense - Shuyang Gao

    Mon, May 07, 2018 @ 12:00 PM - 02:00 PM

    Computer Science

    University Calendar




    Title: Mutual Information Estimation and Its Applications to Machine Learning

    PhD Candidate: Shuyang Gao

    Date: May 7

    Time: 12pm

    Location: SOS B37


    Committee: Aram Galstyan, Greg Verg Steeg, Ilias Diakonikolas, Aiichiro Nakano, Roger Ghanem

    Abstract:
    Mutual information (MI) has been successfully applied to a wide variety of domains due to its remarkable property to measure dependencies between random variables. Despite its popularity and wide spread usage, a common unavoidable problem of mutual information is its estimation. In this thesis, we demonstrate that a popular class of nonparametric MI estimators based on k-nearest-neighbor graphs requires number of samples that scales exponentially with the true MI. Consequently, accurate estimation of MI between strongly dependent variables is possible only for prohibitively large sample size. This important yet overlooked shortcoming of the existing estimators is due to their implicit reliance on local uniformity of the underlying joint distribution. As a result, my thesis proposes two new estimation strategies to address this issue. The new estimators are robust to local non-uniformity, works well with limited data, and is able to capture relationship strengths over many orders of magnitude than the existing k-nearest-neighbor methods.

    Modern data mining and machine learning presents us with problems which may contain thousands of variables and we need to identify only the most promising strong relationships. Therefore, caution must be taken when applying mutual information to such real-world scenarios. By taking these concerns into account, my thesis then demonstrates the practical applicability of mutual information on several tasks, and our contributions include
    i) an information-theoretic framework for measuring stylistic coordination in dialogues. The proposed measure has a simple predictive interpretation and can account for various confounding factors through proper conditioning ii) an new algorithm for mutual information-based feature selection in supervised learning setting iii) an information-theoretic framework for learning disentangled and interpretable representations in unsupervised setting using deep neural networks. For the latter two tasks, we propose to use a variational lower bound for efficient estimation and optimization of mutual information. And for the last task, we have also made a substantial connection of the learning objective with variational auto-encoders (VAE).

    Location: Social Sciences Building (SOS) - B37

    Audiences: Everyone Is Invited

    Posted By: Lizsl De Leon

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  • PhD Defense - Kuan Liu

    Mon, May 07, 2018 @ 01:00 PM - 03:00 PM

    Computer Science

    University Calendar



    PhD Candidate: Kuan Liu

    Title: Scalable machine learning algorithms for implicit feedback based recommendation

    Committee: Prem Natarajan, Kevin Knight, Shri Narayanan (outside member)


    SAL 322
    May 7
    1-3pm


    Abstract:

    Whether in e-commerce, social networks, online music and TV, and many other modern online services, item recommendation stands out to be one of the most important algorithmic components. It recommends items to users that are useful and relevant. It makes huge economic values and is an important information filtering tool.

    The primary goal of this thesis research is to provide machine learning solutions to item recommendation in large scale. The ever-increasing data volume and rich data formats have created a big gap between the requirements of modern recommender systems and our algorithm ability to handle large scale tasks. We work towards efficient personalized ranking algorithms to handle large data volume and advance content-based approaches to incorporate rich side information.

    The thesis work mainly focuses on the following aspects towards this goal: (1) Novel ranking algorithms to deal with large itemsets (2) Deep learning methods to model sequential properties of user feedback (3) To incorporate heterogeneous attributes (4) To fuse signals from multiple modalities. In this talk, I will provide a brief overview of item recommendation history and our contributions. I will discuss our recent work on batch-based ranking algorithms for recommendation from large itemsets and our new methods to fuse signals from multiple modalities.

    Location: Henry Salvatori Computer Science Center (SAL) - 322

    Audiences: Everyone Is Invited

    Posted By: Lizsl De Leon

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