Search Results for author:
Found 42 papers, 18 papers with code
Predicting Modality in Financial Dialogue
In this paper, we perform modality prediction in financial dialogue.
Exploring Morality in Argumentation
Sentiment and stance are two important concepts for the analysis of arguments.
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.
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.
Unreflected Use of Tabular Data Repositories Can Undermine Research Quality
Data repositories have accumulated a large number of tabular datasets from various domains.
A*Net and NBFNet Learn Negative Patterns on Knowledge Graphs
In this technical report, we investigate the predictive performance differences of a rule-based approach and the GNN architectures NBFNet and A*Net with respect to knowledge graph completion.
Reevaluation of Inductive Link Prediction
Within this paper, we show that the evaluation protocol currently used for inductive link prediction is heavily flawed as it relies on ranking the true entity in a small set of randomly sampled negative entities.
Fact Probability Vector Based Goal Recognition
We present a new approach to goal recognition that involves comparing observed facts with their expected probabilities.
AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation
Business process simulation (BPS) is a versatile technique for estimating process performance across various scenarios.
Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization
In this paper, we introduce SYMPOL, a novel method for SYMbolic tree-based on-POLicy RL.
Analytical Uncertainty-Based Loss Weighting in Multi-Task Learning
With the rise of neural networks in various domains, multi-task learning (MTL) gained significant relevance.
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data
Our framework is available under: https://github. com/atschalz/dc_tabeval.
Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo Methods
Neural networks often assume independence among input data samples, disregarding correlations arising from inherent clustering patterns in real-world datasets (e. g., due to different sites or repeated measurements).
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
To address these challenges, we introduce Temporal Graph Benchmark 2. 0 (TGB 2. 0), a novel benchmarking framework tailored for evaluating methods for predicting future links on Temporal Knowledge Graphs and Temporal Heterogeneous Graphs with a focus on large-scale datasets, extending the Temporal Graph Benchmark.
History repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting
Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in Knowledge Graphs for future timesteps based on a history of Knowledge Graphs.
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.
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.
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.
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.
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.
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.
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?
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.
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.
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.
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.
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.
Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion
In this paper, we are concerned with two extensions of AnyBURL.
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.
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.
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.
Word Embeddings-Based Uncertainty Detection in Financial Disclosures
In this paper, we use NLP techniques to detect linguistic uncertainty in financial disclosures.
Sentence Alignment Methods for Improving Text Simplification Systems
We provide several methods for sentence-alignment of texts with different complexity levels.
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.
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.
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.
