Search Results for author:
Found 31 papers, 10 papers with code
RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models
RockIt is a maximum a-posteriori (MAP) query engine for statistical relational models.
Towards Log-Linear Logics with Concrete Domains
We present $\mathcal{MEL}^{++}$ (M denotes Markov logic networks) an extension of the log-linear description logics $\mathcal{EL}^{++}$-LL with concrete domains, nominals, and instances.
Using Abduction in Markov Logic Networks for Root Cause Analysis
We propose a new approach for calculating the root cause for an observed failure in an IT infrastructure.
Sentence Alignment Methods for Improving Text Simplification Systems
We provide several methods for sentence-alignment of texts with different complexity levels.
Word Embeddings-Based Uncertainty Detection in Financial Disclosures
In this paper, we use NLP techniques to detect linguistic uncertainty in financial disclosures.
Political Text Scaling Meets Computational Semantics
During the last fifteen years, automatic text scaling has become one of the key tools of the Text as Data community in political science.
A Spreading Activation Framework for Tracking Conceptual Complexity of Texts
We propose an unsupervised approach for assessing conceptual complexity of texts, based on spreading activation.
A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction
In computer vision research, the process of automating architecture engineering, Neural Architecture Search (NAS), has gained substantial interest.
Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion
In this paper, we are concerned with two extensions of AnyBURL.
Neural Architecture Performance Prediction Using Graph Neural Networks
In computer vision research, the process of automating architecture engineering, Neural Architecture Search (NAS), has gained substantial interest.
Knowledge Graphs meet Moral Values
Operationalizing morality is crucial for understanding multiple aspects of society that have moral values at their core {--} such as riots, mobilizing movements, public debates, etc.
xRAI: Explainable Representations through AI
The approach is based on the idea of training a so-called interpretation network that receives the weights and biases of the trained network as input and outputs the numerical representation of the function the network was supposed to learn that can be directly translated into a symbolic representation.
Why a Naive Way to Combine Symbolic and Latent Knowledge Base Completion Works Surprisingly Well
We compare a rule-based approach for knowledge graph completion against current state-of-the-art, which is based on embbedings.
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.
Top-Down Influence? Predicting CEO Personality and Risk Impact from Speech Transcripts
How much does a CEO's personality impact the performance of their company?
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.
Outlier Explanation via Sum-Product Networks
Existing methods are based on beam search in the space of feature subsets.
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.
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.
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.
Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?
In this paper, we show that it does not provide any benefit to use landmarks that are part of the initial state in a planning landmark based goal recognition approach.
Learning Disentangled Discrete Representations
Recent successes in image generation, model-based reinforcement learning, and text-to-image generation have demonstrated the empirical advantages of discrete latent representations, although the reasons behind their benefits remain unclear.
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.
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.
PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances
We present PGTNet, an approach that transforms event logs into graph datasets and leverages graph-oriented data for training Process Graph Transformer Networks to predict the remaining time of business process instances.
Exploring Morality in Argumentation
Sentiment and stance are two important concepts for the analysis of arguments.
Unsupervised stance detection for arguments from consequences
To address this limitation, we propose a topic independent approach that focuses on a frequently encountered class of arguments, specifically, on arguments from consequences.
Predicting Modality in Financial Dialogue
In this paper, we perform modality prediction in financial dialogue.
Come hither or go away? Recognising pre-electoral coalition signals in the news
In this paper, we introduce the task of political coalition signal prediction from text, that is, the task of recognizing from the news coverage leading up to an election the (un)willingness of political parties to form a government coalition.
