Alchemy
Alchemy is a software package providing a series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov logic representation. Alchemy allows you to easily develop a wide range of AI applications, including:
- Collective classification
- Link prediction
- Entity resolution
- Social network modeling
- Information extraction
People
Publications
- Abductive Markov Logic for Plan Recognition (2011)
- Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models (2011)
- Parallel Coordinate Descent for L1-Regularized Loss Minimization (2011)
- Implementing Weighted Abduction in Markov Logic (2011)
- A tutorial introuction to Bayesian models of cognitive development (2011)
- Approximation by Quantization (2011)
- Constraint Propagation for Efficient Inference in Markov Logic (2011)
- Fault identification via non-parametric belief propagation (2011)
- How to Grow a Mind: Statistics, Structure, and Abstraction (2011)
- Learning Probabilistic Behavior Models in Real-time Strategy Games (2011)
- Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees (2011)
- Probabilistic Theorem Proving (2011)
- Sum-Product Networks: A New Deep Architecture (2011)
- When Did You Start Doing That Thing That You Do? Interactive Activity Recognition and Prompting (2011)
- Learning Efficient Markov Networks (2010)
- Modeling and Reasoning about Success, Failure, and Intent of Multi-Agent Activities (2010)
- Exploiting Logical Structure in Lifted Probabilistic Inference (2010)
- Leveraging Ontologies for Lifted Probabilistic Inference and Learning (2010)
- Machine Reading: A "Killer App" for Statistical Relational AI (2010)
- Online Max-Margin Weight Learning with Markov Logic Networks (2010)
- Recognizing Multi-Agent Activities from GPS Data (2010)
- Learning Markov Logic Networks Using Structural Motifs (2010)
- Connecting the Dots Between News Articles (2010)
- A Language for Relational Decision Theory (2009)
- Deep Transfer via Second-Order Markov Logic (2009)
- Integrating Logic and Probability: Algorithmic Improvements in Markov Logic Networks (2009)
- Learning Markov Logic Network Structure via Hypergraph Lifting (2009)
- Probabilistic Abduction using Markov Logic Networks (2009)
- A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC (2008)
- Hybrid Markov Logic Networks (2008)
- Lifted First-Order Belief Propagation (2008)
- Discovery of Social Relationships in Consumer Photo Collections using Markov Logic (2008)
- Extracting Semantic Networks from Text via Relational Clustering (2008)
- Joint Inference in Information Extraction (2007)
- Recursive Random Fields (2007)
- Efficient Weight Learning for Markov Logic Networks (2007)
- Extending Markov Logic to Model Probability Distributions in Relational Domains (2007)
- Markov Logic in Infinite Domains (2007)
- Statistical Predicate Invention (2007)
- Structured Machine Learning: Ten Problems for the Next Ten Years (2007)
- Entity Resolution with Markov Logic (2006)
- Markov Logic Networks (2006)
- Memory-Efficient Inference in Relational Domains (2006)
- Sound and Efficient Inference with Probabilistic and Deterministic Dependencies (2006)
- Discriminative Training of Markov Logic Networks (2005)
- Learning the Structure of Markov Logic Networks (2005)
Research Groups
Last changed Fri, 2012-11-16 11:28

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