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The VLDB journal : the international journal on very large data bases.
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The article was published on 1992-01-01 and is currently open access. It has received 335 citations till now. The article focuses on the topics: Very large database.read more
Citations
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Journal Article
Towards Trajectory Anonymization: a Generalization-Based Approach
TL;DR: A utility metric that maximizes the probability of a good representation and a novel generalization-based approach that applies to trajectories and sequences in general are presented and proposed to address time and space sensitive applications.
Journal ArticleDOI
A Proposal for a Coordinated Effort for the Determination of Brainwide Neuroanatomical Connectivity in Model Organisms at a Mesoscopic Scale
Jason W. Bohland,Caizhi Wu,Helen Barbas,Hemant Bokil,Mihail Bota,Hans C. Breiter,Hollis T. Cline,John Doyle,Peter J. Freed,Ralph J. Greenspan,Suzanne N. Haber,Michael Hawrylycz,Daniel G. Herrera,Claus C. Hilgetag,Z. Josh Huang,Allan R. Jones,Edward G. Jones,Harvey J. Karten,David Kleinfeld,Rolf Kötter,Henry A. Lester,John M. Lin,Brett D. Mensh,Shawn Mikula,Jaak Panksepp,Joseph L. Price,Joseph Safdieh,Clifford B. Saper,Nicholas D. Schiff,Jeremy D. Schmahmann,Bruce Stillman,Karel Svoboda,Larry W. Swanson,Arthur W. Toga,David C. Van Essen,James D. Watson,Partha P. Mitra +36 more
TL;DR: A concerted effort is advocated for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brainwide coverage, using injections of tracers or viral vectors.
Proceedings Article
Generative probabilistic novelty detection with adversarial autoencoders
TL;DR: In this paper, a probabilistic approach is proposed to estimate the likelihood that a sample was generated by the inlier distribution, and the probability factorizes and can be computed with respect to local coordinates of the manifold tangent space.
Proceedings ArticleDOI
Anytime Bottom-Up Rule Learning for Knowledge Graph Completion
TL;DR: Reinforcement learning is introduced to better guide the sampling process of AnyBURL and it is found out that reinforcement learning helps finding more valuable rules earlier in the search process.
Proceedings Article
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs
TL;DR: DRUM is proposed, a scalable and differentiable approach for mining first-order logical rules from knowledge graphs that resolves the problem of learning probabilistic logical rules for inductive and interpretable link prediction.
References
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Journal ArticleDOI
A Proposal for a Coordinated Effort for the Determination of Brainwide Neuroanatomical Connectivity in Model Organisms at a Mesoscopic Scale
Jason W. Bohland,Caizhi Wu,Helen Barbas,Hemant Bokil,Mihail Bota,Hans C. Breiter,Hollis T. Cline,John Doyle,Peter J. Freed,Ralph J. Greenspan,Suzanne N. Haber,Michael Hawrylycz,Daniel G. Herrera,Claus C. Hilgetag,Z. Josh Huang,Allan R. Jones,Edward G. Jones,Harvey J. Karten,David Kleinfeld,Rolf Kötter,Henry A. Lester,John M. Lin,Brett D. Mensh,Shawn Mikula,Jaak Panksepp,Joseph L. Price,Joseph Safdieh,Clifford B. Saper,Nicholas D. Schiff,Jeremy D. Schmahmann,Bruce Stillman,Karel Svoboda,Larry W. Swanson,Arthur W. Toga,David C. Van Essen,James D. Watson,Partha P. Mitra +36 more
TL;DR: A concerted effort is advocated for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brainwide coverage, using injections of tracers or viral vectors.
Proceedings ArticleDOI
Towards trajectory anonymization: a generalization-based approach
TL;DR: Wang et al. as mentioned in this paper proposed a generalization-based approach that applies to trajectories and sequences in general and proposed trajectory anonymization techniques to address time and space sensitive applications.
Proceedings ArticleDOI
Anytime Bottom-Up Rule Learning for Knowledge Graph Completion
TL;DR: Reinforcement learning is introduced to better guide the sampling process of AnyBURL and it is found out that reinforcement learning helps finding more valuable rules earlier in the search process.
Proceedings ArticleDOI
Leveraging data and structure in ontology integration
TL;DR: This paper presents a new algorithm (ILIADS) that tightly integrates both data matching and logical reasoning to achieve better matching of ontologies and compares against two systems - the ontology matching tool FCA-merge and the schema matching tool COMA++.
Posted Content
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
TL;DR: This work makes the computation of the novelty probability feasible because it linearize the parameterized manifold capturing the underlying structure of the inlier distribution, and shows how the probability factorizes and can be computed with respect to local coordinates of the manifold tangent space.