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Found 13 papers, 2 papers with code
Question Modifiers in Visual Question Answering
Visual Question Answering (VQA) is a challenge problem that can advance AI by integrating several important sub-disciplines including natural language understanding and computer vision.
Verifying Relational Explanations: A Probabilistic Approach
Explanations on relational data are hard to verify since the explanation structures are more complex (e. g. graphs).
Mastery Guided Non-parametric Clustering to Scale-up Strategy Prediction
Predicting the strategy (sequence of concepts) that a student is likely to use in problem-solving helps Adaptive Instructional Systems (AISs) better adapt themselves to different types of learners based on their learning abilities.
On the verification of Embeddings using Hybrid Markov Logic
The standard approach to verify representations learned by Deep Neural Networks is to use them in specific tasks such as classification or regression, and measure their performance based on accuracy in such tasks.
Scalable and Equitable Math Problem Solving Strategy Prediction in Big Educational Data
The strategy prediction model is trained on instances sampled from these clusters.
CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space
To address these issues, in this paper, we propose an approach to extract a very small number of aggregated features that are easy to interpret and compute, and empirically show that we obtain high prediction accuracy even with a significantly reduced feature-space.
Joint Inference for Event Coreference Resolution
Event coreference resolution is a challenging problem since it relies on several components of the information extraction pipeline that typically yield noisy outputs.
Joint Inference for Mode Identification in Tutorial Dialogues
Our results show that the joint inference system is far more effective than the pipeline system in mode detection, and improves over the performance of the pipeline system by about 6 points in F1 score.
An Integer Polynomial Programming Based Framework for Lifted MAP Inference
In this paper, we present a new approach for lifted MAP inference in Markov logic networks (MLNs).
Scaling-up Importance Sampling for Markov Logic Networks
Second, they suffer from the evidence problem, which arises because evidence breaks symmetries, severely diminishing the power of lifted inference.
Dynamic Blocking and Collapsing for Gibbs Sampling
Our dynamic algorithm periodically updates the partitioning into blocked and collapsed variables by leveraging correlation statistics gathered from the generated samples and enables rapid mixing by blocking together and collapsing highly correlated variables.
On Lifting the Gibbs Sampling Algorithm
Statistical relational learning models combine the power of first-order logic, the de facto tool for handling relational structure, with that of probabilistic graphical models, the de facto tool for handling uncertainty.
