31,676 research outputs found
Building Program Vector Representations for Deep Learning
Deep learning has made significant breakthroughs in various fields of
artificial intelligence. Advantages of deep learning include the ability to
capture highly complicated features, weak involvement of human engineering,
etc. However, it is still virtually impossible to use deep learning to analyze
programs since deep architectures cannot be trained effectively with pure back
propagation. In this pioneering paper, we propose the "coding criterion" to
build program vector representations, which are the premise of deep learning
for program analysis. Our representation learning approach directly makes deep
learning a reality in this new field. We evaluate the learned vector
representations both qualitatively and quantitatively. We conclude, based on
the experiments, the coding criterion is successful in building program
representations. To evaluate whether deep learning is beneficial for program
analysis, we feed the representations to deep neural networks, and achieve
higher accuracy in the program classification task than "shallow" methods, such
as logistic regression and the support vector machine. This result confirms the
feasibility of deep learning to analyze programs. It also gives primary
evidence of its success in this new field. We believe deep learning will become
an outstanding technique for program analysis in the near future.Comment: This paper was submitted to ICSE'1
Bayesian Agglomerative Clustering with Coalescents
We introduce a new Bayesian model for hierarchical clustering based on a
prior over trees called Kingman's coalescent. We develop novel greedy and
sequential Monte Carlo inferences which operate in a bottom-up agglomerative
fashion. We show experimentally the superiority of our algorithms over others,
and demonstrate our approach in document clustering and phylolinguistics.Comment: NIPS 200
An Algorithm For Building Language Superfamilies Using Swadesh Lists
The main contributions of this thesis are the following: i. Developing an algorithm to generate language families and superfamilies given for each input language a Swadesh list represented using the international phonetic alphabet (IPA) notation. ii. The algorithm is novel in using the Levenshtein distance metric on the IPA representation and in the way it measures overall distance between pairs of Swadesh lists. iii. Building a Swadesh list for the author\u27s native Kinyarwanda language because a Swadesh list could not be found even after an extensive search for it.
Adviser: Peter Reves
Growing Story Forest Online from Massive Breaking News
We describe our experience of implementing a news content organization system
at Tencent that discovers events from vast streams of breaking news and evolves
news story structures in an online fashion. Our real-world system has distinct
requirements in contrast to previous studies on topic detection and tracking
(TDT) and event timeline or graph generation, in that we 1) need to accurately
and quickly extract distinguishable events from massive streams of long text
documents that cover diverse topics and contain highly redundant information,
and 2) must develop the structures of event stories in an online manner,
without repeatedly restructuring previously formed stories, in order to
guarantee a consistent user viewing experience. In solving these challenges, we
propose Story Forest, a set of online schemes that automatically clusters
streaming documents into events, while connecting related events in growing
trees to tell evolving stories. We conducted extensive evaluation based on 60
GB of real-world Chinese news data, although our ideas are not
language-dependent and can easily be extended to other languages, through
detailed pilot user experience studies. The results demonstrate the superior
capability of Story Forest to accurately identify events and organize news text
into a logical structure that is appealing to human readers, compared to
multiple existing algorithm frameworks.Comment: Accepted by CIKM 2017, 9 page
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