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Found 15 papers, 4 papers with code
GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
Our method combines axis-aligned splits, which is a useful inductive bias for tabular data, with the flexibility of gradient-based optimization.
On the Aggregation of Rules for Knowledge Graph Completion
Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models.
Towards Machine Learning-based Fish Stock Assessment
The accurate assessment of fish stocks is crucial for sustainable fisheries management.
GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent
Decision Trees (DTs) are commonly used for many machine learning tasks due to their high degree of interpretability.
Leveraging Planning Landmarks for Hybrid Online Goal Recognition
Furthermore, we show that the utilized planning landmark based approach, which was so far only evaluated on artificial benchmark domains, achieves also good recognition performance when applied to a real-world cooking scenario.
Investigating the Combination of Planning-Based and Data-Driven Methods for Goal Recognition
We empirically show that the proposed extension not only outperforms the purely planning-based- and purely data-driven goal recognition methods but is also able to recognize the correct goal more reliably, especially when only a small number of observations were seen.
Outlier Explanation via Sum-Product Networks
Existing methods are based on beam search in the space of feature subsets.
Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition
We propose to identify such segments of similar behavior via clustering the distributions of annotations of the time segments.
Explaining Neural Networks without Access to Training Data
We consider generating explanations for neural networks in cases where the network's training data is not accessible, for instance due to privacy or safety issues.
Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs
We study sensor-based human activity recognition in manual work processes like assembly tasks.
Human Activity Recognition using Attribute-Based Neural Networks and Context Information
Here, we show how such context information can be integrated systematically into a deep neural network-based HAR system.
Exchangeability-Aware Sum-Product Networks
On the other hand, mixtures of exchangeable variable models (MEVMs) are a class of tractable probabilistic models that make use of exchangeability of discrete random variables to render inference tractable.
State-Space Abstractions for Probabilistic Inference: A Systematic Review
Furthermore, we provide new high-level categories that classify the approaches, based on common properties of the approaches.
Lifted Filtering via Exchangeable Decomposition
We present a model for exact recursive Bayesian filtering based on lifted multiset states.
Sequential Lifted Bayesian Filtering in Multiset Rewriting Systems
We sketch a novel inference algorithm that provides a solution by incorporating concepts from Lifted Inference algorithms, Probabilistic Multiset Rewriting Systems, and Computational State Space Models.
