Open AccessPosted Content
Incremental Knowledge Base Construction Using DeepDive
TL;DR: This work describes DeepDive, a system that combines database and machine learning ideas to help develop KBC systems, and presents techniques to make the KBC process more efficient, and proposes two methods for incremental inference, based, respectively, on sampling and variational techniques.
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Abstract: Populating a database with unstructured information is a long-standing problem in industry and research that encompasses problems of extraction, cleaning, and integration. Recent names used for this problem include dealing with dark data and knowledge base construction (KBC). In this work, we describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems, and we present techniques to make the KBC process more efficient. We observe that the KBC process is iterative, and we develop techniques to incrementally produce inference results for KBC systems. We propose two methods for incremental inference, based respectively on sampling and variational techniques. We also study the tradeoff space of these methods and develop a simple rule-based optimizer. DeepDive includes all of these contributions, and we evaluate DeepDive on five KBC systems, showing that it can speed up KBC inference tasks by up to two orders of magnitude with negligible impact on quality.
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•Posted Content
Concrete Problems in AI Safety
TL;DR: A list of five practical research problems related to accident risk, categorized according to whether the problem originates from having the wrong objective function, an objective function that is too expensive to evaluate frequently, or undesirable behavior during the learning process, are presented.
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Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
TL;DR: In this paper, the authors survey the current research on applying deep learning to clinical tasks based on EHR data, where they find a variety of deep learning techniques and frameworks being applied to several types of clinical applications including information extraction, representation learning, outcome prediction, phenotyping, and deidentification.
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
TL;DR: This review surveys the current research on applying deep learning to clinical tasks based on EHR data, where a variety of deep learning techniques and frameworks are being applied to several types of clinical applications including information extraction, representation learning, outcome prediction, phenotyping, and deidentification.
•Proceedings Article
SimplE embedding for link prediction in knowledge graphs
Seyed Mehran Kazemi,David Poole +1 more
- 03 Dec 2018
TL;DR: It is proved SimplE is fully expressive and derive a bound on the size of its embeddings for full expressivity and shown empirically that, despite its simplicity, SimplE outperforms several state-of-the-art tensor factorization techniques.
HoloClean: holistic data repairs with probabilistic inference
Theodoros Rekatsinas,Xu Chu,Ihab F. Ilyas,Christopher Ré +3 more
- 01 Aug 2017
TL;DR: A series of optimizations are introduced which ensure that inference over HoloClean's probabilistic model scales to instances with millions of tuples, and yields an average F1 improvement of more than 2× against state-of-the-art methods.
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Probability theory : the logic of science
TL;DR: In this article, a survey of elementary applications of probability theory can be found, including the following: 1. Plausible reasoning 2. The quantitative rules 3. Elementary sampling theory 4. Elementary hypothesis testing 5. Queer uses for probability theory 6. Elementary parameter estimation 7. The central, Gaussian or normal distribution 8. Sufficiency, ancillarity, and all that 9. Repetitive experiments, probability and frequency 10. Advanced applications: 11. Discrete prior probabilities, the entropy principle 12. Simple applications of decision theory 15.
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•Book
Graphical Models, Exponential Families, and Variational Inference
Martin J. Wainwright,Michael I. Jordan +1 more
- 16 Dec 2008
TL;DR: The variational approach provides a complementary alternative to Markov chain Monte Carlo as a general source of approximation methods for inference in large-scale statistical models.
A Convolutional Neural Network for Modelling Sentences
Nal Kalchbrenner,Edward Grefenstette,Phil Blunsom +2 more
- 08 Apr 2014
TL;DR: A convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) is described that is adopted for the semantic modelling of sentences and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations.
Automatic acquisition of hyponyms from large text corpora
Marti A. Hearst
- 23 Aug 1992
TL;DR: A set of lexico-syntactic patterns that are easily recognizable, that occur frequently and across text genre boundaries, and that indisputably indicate the lexical relation of interest are identified.
Distant supervision for relation extraction without labeled data
Mike D. Mintz,Steven Bills,Rion Snow,Dan Jurafsky +3 more
- 02 Aug 2009
TL;DR: This work investigates an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACE-style algorithms, and allowing the use of corpora of any size.