All Seminars
| Title: Finding a Happy Medium between Accuracy and Speed for Dependency Parsing |
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| Colloquium: N/A |
| Speaker: Jinho Choi of University of Massachusetts Amherst |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2014-03-06 at 4:00PM |
| Venue: W306 |
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| Abstract: Why is Natural Language Processing interesting? What makes NLP hard? How can we bring NLP research to practice? These are all open-ended questions. In this talk, I present a novel approach called selectional branching, which optimizes both accuracy and speed for one of core NLP tasks, dependency parsing. Our approach uses confidence estimates to decide when to employ a beam, providing the accuracy of beam search at speeds close to a greedy dependency parsing approach. Selectional branching is guaranteed to perform faster than beam search yet performs as accurately. With the benchmark setup in English, our parser shows an accuracy of 92.96% and a speed of 9 milliseconds per sentence, which is faster and more accurate than the previous state-of-the-art transition-based parser using beam search. It also outperforms other dependency parsers using beam search, dynamic programming, integer linear programming, etc. for languages such as Danish, Dutch, Slovene, and Swedish. |
| Title: Towards Large Scale Open Domain Natural Language Processing |
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| Colloquium: N/A |
| Speaker: Gourab Kundu of University of Illinois |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2014-03-05 at 3:00PM |
| Venue: MSC W201 |
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| Abstract: Machine Learning and Inference methods are becoming ubiquitous ñ a broad range of scientific advances and technologies rely on machine learning techniques. In particular, the big data revolution heavily depends on our ability to use statistical machine learning methods to make sense of the large amounts of data we have. Research in Natural Language Processing has both benefited and contributed to the advancement of machine learning and inference methods. However multiple problems still hinder the broad application of some of these methods. Performance Degradation of machine learning based systems in domains other than the training domain is one of the key problems hindering widespread deployment of these systems.\\ \\ In this talk, I will present techniques for domain adaptation "on the fly", that allows adaptation to test domains using the same model from training domain. This is accomplished by transforming text from the test domain to look more like the training domain and running the same model from the training domain. This process of text adaptation treats the model as black box, thus makes the adaptation of complex pipelines of models easy and flexible. The next key challenge for machine learning is the processing of vast amounts of data in an efficient manner. Prediction problems for tools are often complicated, for natural language processing and other disciplines, making application of these tools to big data infeasible. The later part of the talk will focus on improving the scalability of machine learning tools with complex prediction component to meet the challenges of big data. I will show how it is possible to amortize the cost of prediction over the lifetime of any machine learning tool. Particularly, I will focus on amortizing integer linear programs which can represent a wide variety of prediction problems. I will present exact and approximate theorems for speeding up the solution time of new integer programs by reusing solutions of previously solved integer programs.\\ \\ Gourab Kundu is a doctoral candidate in Computer Science Department of University of Illinois at Urbana-Champaign, supervised by Prof. Dan Roth. He has also worked in IBM research and Google for summer internships. He is broadly interested in all aspects of machine learning and natural language processing. He has publications in top tier natural language processing conferences along with a best student paper in CoNLL 2011. |
| Title: Bounded gaps between primes in Chebotarev sets |
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| Seminar: Algebra |
| Speaker: Jesse Thorner of Emory University |
| Contact: David Zureick-Brown, dzb@mathcs.emory.edu |
| Date: 2014-03-04 at 4:00PM |
| Venue: W302 |
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| Abstract: A new and exciting breakthrough due to Maynard establishes that there exist infinitely many pairs of primes $p_1,p_2$ with $|p_1-p_2|\leq 600$ as a consequence of the Bombieri-Vinogradov Theorem. In this paper, we apply their general method to the setting of Chebotarev sets of primes. We study applications of these bounded gaps with an emphasis on ranks of prime quadratic twists of elliptic curves over $\mathbb{Q}$. |
| Title: Weights and Measures: Fast Prediction in an Era of Big-Data |
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| Colloquium: N/A |
| Speaker: Lev Reyzin of University of Illinois at Chicago |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2014-03-04 at 4:00PM |
| Venue: MSC W303 |
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| Abstract: In this talk I will discuss algorithms I have developed for learning in a world where data is abundant and predicting quickly and accurately is essential. In particular, I will focus on some recent work on modern variants of supervised and bandit learning. One common element of the algorithms I will present is that they nontrivially improve upon classical weighing and sampling methods to produce provable and practical improvements over traditional approaches. |
| Title: Two-Hilbert spaces Mourre theory for the Laplace-Beltrami operator on manifolds with asymptotically cylindrical ends |
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| Seminar: Analysis and Differential Geometry |
| Speaker: Rafael Tiedra de Aldecoa of Catholic University of Chile |
| Contact: David Borthwick, davidb@mathcs.emory.edu |
| Date: 2014-03-04 at 4:00PM |
| Venue: MSC W301 |
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| Abstract: We review some aspects of Mourre theory in a two-Hilbert spaces setting. Then we apply this theory to the spectral analysis for the Laplace-Beltrami operator on manifolds with asymptotically cylindrical ends. This is a joint work with Serge Richard (University of Nagoya). |
| Title: When Big Data Meets BRAIN Initiative: Large-Scale Structured Sparse Learning with Applications in Imaging Genomics |
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| Colloquium: N/A |
| Speaker: Heng Huang of University of Texas at Arlington |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2014-03-03 at 4:00PM |
| Venue: MSC W303 |
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| Abstract: Sparsity is one of the intrinsic properties of real-world data, thus sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability at low computational cost, and provide great opportunities to analyze the big, complex, and diverse datasets. By enforcing properly designed structured sparsity, we can integrate the specific data structures and domain knowledge into the machine learning models to simplify data models and discover predictive patterns in big data analytics. Big Data research is accelerating the translation of biological and biomedical data to advance the detection, diagnosis, treatment and prevention of diseases, including the recently announced BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative. To address the challenging problems in current big data mining, we proposed several novel large-scale structured sparse learning models for multi-dimensional data integration, heterogeneous multi-task learning, group/graph structured data analysis, and longitudinal feature learning. We applied our new structured sparse learning models to analyze the multi-modal neuroimaging and genome-wide array data in Imaging Genomics and discover the phenotypic and genotypic biomarkers to characterize the neurodegenerative process in the progression of Alzheimer’s disease and other complex brain disorders. We also utilized our new machine learning models to analyze the Electronic Medical Records for predicting the heart failure patients’ readmission and drug side effects, detect the multi-dimensional biomarkers in The Cancer Genome Atlas (TCGA) research, and identify the brain circuitry patterns in Human Connectome. |
| Title: An Integrated Human Decision Making Model under Extended Belief-Desire-Intention Framework: Emergency Evacuation Applications |
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| Seminar: Computer Science |
| Speaker: Young-Jun Son of The University of Arizona |
| Contact: TBA |
| Date: 2014-02-28 at 11:00AM |
| Venue: MSC W303 |
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| Abstract: In this talk, we discuss an integrated Belief-Desire-Intention (BDI) modeling framework for human decision making, whose sub-modules are based on Bayesian belief network, Decision-Field-Theory, and probabilistic depth first search technique. A key novelty of the proposed model is its ability to represent both the human decision-making and decision-planning functions in a unified framework. In this talk, the proposed modeling framework is demonstrated for human’s evacuation behaviors under a terrorist bomb attack situation. To mimic realistic human behaviors, attributes of the BDI framework are reverse-engineered from the human-in-the-loop experiments conducted in the Cave Automatic Virtual Environment (CAVE) available at The University of Arizona. A crowd simulation is then constructed, where individual human behaviors are based on what was learned from the CAVE experiments. In this work, the simulated environment and humans conforming to the proposed BDI framework are implemented in AnyLogic® agent-based simulation software, where each human entity calls external Netica BBN software to perform its perceptual processing function and Soar software to perform its real-time planning and decision-execution functions. The constructed crowd simulation is then used to test impact of several factors (e.g. demographics of people, number of policemen, information sharing via speakers) on evacuation performance (e.g. average evacuation time, percentage of casualties). Finally, we discuss other emergency evacuation applications (e.g. evacuation behaviors under fire in a factory) and research extensions for the proposed BDI framework. |
| Title: Assured Information Distillation in Social Sensing |
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| Colloquium: N/A |
| Speaker: Dong Wang of University of Illinois at Urbana-Champaign |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2014-02-27 at 4:00PM |
| Venue: W306 |
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| Abstract: The advent of sensors and online social broadcast media (e.g., Twitter and Flickr) create a deluge of unfiltered, unstructured, and unvetted data about the physical environment. This opens up unprecedented challenges and opportunities in social sensing, where the goal is to distill assured information from social sources and devices in their possession. This talk will present a new analytical framework and theories to obtain reliable information with quality guarantees from large amounts of unreliable social sensing data. Noticeably, our analytical framework is the first to jointly model the complex interactions among three deeply coupled networks underlying the data; namely, the information, social and physical networks. The talk will also introduce a new information distillation system we built, called Apollo, which has been applied in a wide range of social sensing scenarios such as real event/disaster tracking, geo-tagging, smart road applications, and language/dialect classification. Apollo is now used by different branches at Army Research Lab (ARL). |
| Title: Solved and unsolved problems in elementary number theory |
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| Seminar: Joint Athens-Atlanta Number Theory Seminar |
| Speaker: Paul Pollack of UGA |
| Contact: David Zureick-Brown, dzb@mathcs.emory.edu |
| Date: 2014-02-25 at 4:00PM |
| Venue: W302 |
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| Abstract: This will be a survey of certain easy-to-understand problems in elementary number theory about which "not enough" is known. We will start with a discussion of the infinitude of primes, then discuss the ancient concept of perfect numbers (and related notions), and then branch off into other realms as the spirit of Paul Erd\H{o}s leads us. |
| Title: Discourse-Guided and Multi-faceted Event Recognition from Text |
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| Colloquium: N/A |
| Speaker: Ruihong Huang of University of Utah |
| Contact: Vaidy Sunderam, vss@emory.edu |
| Date: 2014-02-25 at 4:00PM |
| Venue: MSC W303 |
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| Abstract: Events are one important type of information throughout the text. Accurately extracting significant events from large volumes of text informs the government, companies and the public regarding possible changing circumstances caused or implied by events. \\ \\ Extracting event information completely and accurately is challenging mainly due to the high complexity of discourse phenomena. In this talk, I will present two discourse-guided event extraction architectures that explore evidence and clues from wider discourse to seek out or validate pieces of event descriptions. TIER is a multilayered event extraction architecture that performs text analysis at multiple granularities to progressively "zoom in" on relevant event information. LINKER is a more principled discourse-guided approach that models textual cohesion properties in a single structured sentence classifier.\\ \\ Finding documents that describe a specific type of event is also challenging because of the wide variety and ambiguity of event expressions. I will focus on the recent multi-faceted event recognition approach that uses event defining characteristics (facets), in addition to event expressions, to effectively resolve the complexity of event descriptions. I will present a novel bootstrapping algorithm that can automatically learn both event expressions and facets from unannotated texts, which will enable fast configurations of domain-specific event detection systems. |