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Found 15 papers, 9 papers with code
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning
In this paper, we introduce a novel approach to enhance information extraction combining multiple sentence representations and contrastive learning.
CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation
To evaluate the performance of state-of-the-art RC models on the CORE dataset, we conduct experiments in the few-shot domain adaptation setting.
Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques
This paper investigates the transferability of debiasing techniques across different languages within multilingual models.
Timing Process Interventions with Causal Inference and Reinforcement Learning
Our contribution consists of experiments on timed process interventions with synthetic data that renders genuine online RL and the comparison to CI possible, and allows for an accurate evaluation of the results.
Can recurrent neural networks learn process model structure?
In this work, we investigate the capabilities of such an LSTM to actually learn the underlying process model structure of an event log.
Predicting student performance using sequence classification with time-based windows
A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula.
Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors
For realising timely delivery, an accurate prediction of the remaining time of the delivery process is crucial.
Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring
The inability of artificial neural networks to assess the uncertainty of their predictions is an impediment to their widespread use.
Can deep neural networks learn process model structure? An assessment framework and analysis
Therefore, in this work, we propose an evaluation scheme complemented with new fitness, precision, and generalisation metrics, specifically tailored towards measuring the capacity of deep learning models to learn process model structure.
Expert-driven Trace Clustering with Instance-level Constraints
Within the field of process mining, several different trace clustering approaches exist for partitioning traces or process instances into similar groups.
Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoring
Often the training and test sets are not completely separated, a data leakage problem particular to predictive process monitoring.
Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processes
This obliviousness of uncertainty is a major obstacle towards their adoption in practice.
Process Model Forecasting Using Time Series Analysis of Event Sequence Data
To this end, we develop a technique to forecast the entire process model from historical event data.
Process Outcome Prediction: CNN vs. LSTM (with Attention)
Attention is another technique that, in combination with LSTMs, has found application in time series classification and was included in our research.
Predictive Process Model Monitoring using Recurrent Neural Networks
To achieve this, Processes-As-Movies (PAM) is introduced, i. e., a novel technique capable of jointly mining and predicting declarative process constraints between activities in various windows of a process' execution.
