David Poole

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The University of British Columbia

A Probabilistic Approach for Predicting Landslides by Learning a Self-Aligned Deep Convolutional Model

Landslides are movement of soil and rock under the influence of gravity....
11/12/2019 ∙ by Ainaz Hajimoradlou, et al. ∙ 30 ∙ share

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Structure Learning for Relational Logistic Regression: An Ensemble Approach

We consider the problem of learning Relational Logistic Regression (RLR)...
08/06/2018 ∙ by Nandini Ramanan, et al. ∙ 14 ∙ share

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Domain Recursion for Lifted Inference with Existential Quantifiers

In recent work, we proved that the domain recursion inference rule makes...
07/24/2017 ∙ by Seyed Mehran Kazemi, et al. ∙ 0 ∙ share

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Comparing Aggregators for Relational Probabilistic Models

Relational probabilistic models have the challenge of aggregation, where...
07/25/2017 ∙ by Seyed Mehran Kazemi, et al. ∙ 0 ∙ share

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Why is Compiling Lifted Inference into a Low-Level Language so Effective?

First-order knowledge compilation techniques have proven efficient for l...
06/14/2016 ∙ by Seyed Mehran Kazemi, et al. ∙ 0 ∙ share

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New Liftable Classes for First-Order Probabilistic Inference

Statistical relational models provide compact encodings of probabilistic...
10/26/2016 ∙ by Seyed Mehran Kazemi, et al. ∙ 0 ∙ share

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A Learning Algorithm for Relational Logistic Regression: Preliminary Results

Relational logistic regression (RLR) is a representation of conditional ...
06/28/2016 ∙ by Bahare Fatemi, et al. ∙ 0 ∙ share

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Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (1994)

This is the Proceedings of the Tenth Conference on Uncertainty in Artifi...
04/13/2013 ∙ by Ramon Lopez de Mantaras, et al. ∙ 0 ∙ share

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Towards Completely Lifted Search-based Probabilistic Inference

The promise of lifted probabilistic inference is to carry out probabilis...
07/20/2011 ∙ by David Poole, et al. ∙ 0 ∙ share

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RelNN: A Deep Neural Model for Relational Learning

Statistical relational AI (StarAI) aims at reasoning and learning in noi...
12/07/2017 ∙ by Seyed Mehran Kazemi, et al. ∙ 0 ∙ share

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SimplE Embedding for Link Prediction in Knowledge Graphs

The aim of knowledge graphs is to gather knowledge about the world and p...
02/13/2018 ∙ by Seyed Mehran Kazemi, et al. ∙ 0 ∙ share

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Improved Knowledge Graph Embedding using Background Taxonomic Information

Knowledge graphs are used to represent relational information in terms o...
12/07/2018 ∙ by Bahare Fatemi, et al. ∙ 0 ∙ share

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Record Linkage to Match Customer Names: A Probabilistic Approach

Consider the following problem: given a database of records indexed by n...
06/26/2018 ∙ by Bahare Fatemi, et al. ∙ 0 ∙ share

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Knowledge Hypergraphs: Extending Knowledge Graphs Beyond Binary Relations

Knowledge graphs store facts using relations between pairs of entities. ...
06/01/2019 ∙ by Bahare Fatemi, et al. ∙ 0 ∙ share

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David Poole

Featured Co-authors

Guy Van den Broeck

Kristian Kersting

Siamak Ravanbakhsh

David Vazquez

Tushar Khot

Sriraam Natarajan

Fahiem Bacchus

Seyed Mehran Kazemi

Ramon Lopez de Mantaras

Gautam Kunapuli

Angelika Kimmig

A Probabilistic Approach for Predicting Landslides by Learning a Self-Aligned Deep Convolutional Model

Structure Learning for Relational Logistic Regression: An Ensemble Approach

Domain Recursion for Lifted Inference with Existential Quantifiers

Comparing Aggregators for Relational Probabilistic Models

Why is Compiling Lifted Inference into a Low-Level Language so Effective?

New Liftable Classes for First-Order Probabilistic Inference

A Learning Algorithm for Relational Logistic Regression: Preliminary Results

Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (1994)

Towards Completely Lifted Search-based Probabilistic Inference

RelNN: A Deep Neural Model for Relational Learning

SimplE Embedding for Link Prediction in Knowledge Graphs

Improved Knowledge Graph Embedding using Background Taxonomic Information

Record Linkage to Match Customer Names: A Probabilistic Approach

Knowledge Hypergraphs: Extending Knowledge Graphs Beyond Binary Relations