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Found 17 papers, 5 papers with code
Auto-Encoder Neural Network Incorporating X-Ray Fluorescence Fundamental Parameters with Machine Learning
We consider energy-dispersive X-ray Fluorescence (EDXRF) applications where the fundamental parameters method is impractical such as when instrument parameters are unavailable.
Automatic Neural Network Hyperparameter Optimization for Extrapolation: Lessons Learned from Visible and Near-Infrared Spectroscopy of Mango Fruit
To encourage the neural network model to extrapolate, we consider validating model configurations on samples that are shifted in time similar to the test set.
Knowledge Hypergraph Embedding Meets Relational Algebra
Embedding-based methods for reasoning in knowledge hypergraphs learn a representation for each entity and relation.
Binarised Regression with Instance-Varying Costs: Evaluation using Impact Curves
In binarised regression, binary decisions are generated from a learned regression model (or real-valued dependent variable), which is useful when the division between instances that should be predicted positive or negative depends on the utility.
Predicting Landslides Using Contour Aligning Convolutional Neural Networks
Landslides, movement of soil and rock under the influence of gravity, are common phenomena that cause significant human and economic losses every year.
Knowledge Hypergraphs: Prediction Beyond Binary Relations
Knowledge graphs store facts using relations between two entities.
Improved Knowledge Graph Embedding using Background Taxonomic Information
Knowledge graphs are used to represent relational information in terms of triples.
Structure Learning for Relational Logistic Regression: An Ensemble Approach
We consider the problem of learning Relational Logistic Regression (RLR).
Record Linkage to Match Customer Names: A Probabilistic Approach
We provide a probabilistic model using relational logistic regression to find the probability of each record in the database being the desired record for a given query and find the best record(s) with respect to the probabilities.
SimplE Embedding for Link Prediction in Knowledge Graphs
We prove SimplE is fully expressive and derive a bound on the size of its embeddings for full expressivity.
Ranked #19 on
Link Prediction
on FB15k
RelNN: A Deep Neural Model for Relational Learning
Statistical relational AI (StarAI) aims at reasoning and learning in noisy domains described in terms of objects and relationships by combining probability with first-order logic.
Comparing Aggregators for Relational Probabilistic Models
Relational probabilistic models have the challenge of aggregation, where one variable depends on a population of other variables.
Domain Recursion for Lifted Inference with Existential Quantifiers
In this paper, we show that domain recursion can also be applied to models with existential quantifiers.
New Liftable Classes for First-Order Probabilistic Inference
Statistical relational models provide compact encodings of probabilistic dependencies in relational domains, but result in highly intractable graphical models.
A Learning Algorithm for Relational Logistic Regression: Preliminary Results
We compare our learning algorithm to other structure and parameter learning algorithms in the literature, and compare the performance of RLR models to standard logistic regression and RDN-Boost on a modified version of the MovieLens data-set.
Why is Compiling Lifted Inference into a Low-Level Language so Effective?
First-order knowledge compilation techniques have proven efficient for lifted inference.
Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (1994)
This is the Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, which was held in Seattle, WA, July 29-31, 1994
