Search Results for author:
Found 8 papers, 1 papers with code
On the Challenges and Opportunities in Generative AI
The field of deep generative modeling has grown rapidly and consistently over the years.
Reimagining Anomalies: What If Anomalies Were Normal?
Deep learning-based methods have achieved a breakthrough in image anomaly detection, but their complexity introduces a considerable challenge to understanding why an instance is predicted to be anomalous.
Evaluating Dynamic Topic Models
There is a lack of quantitative measures to evaluate the progression of topics through time in dynamic topic models (DTMs).
Text Style Transfer Evaluation Using Large Language Models
This suggests that LLMs could be a feasible alternative to human evaluation and other automated metrics in TST evaluation.
A Call for Standardization and Validation of Text Style Transfer Evaluation
Text Style Transfer (TST) evaluation is, in practice, inconsistent.
Deep Anomaly Detection on Tennessee Eastman Process Data
Our extensive study will facilitate choosing appropriate anomaly detection methods in industrial applications.
Learning to Play Text-based Adventure Games with Maximum Entropy Reinforcement Learning
To deal with sparse extrinsic rewards from the environment, we combine it with a potential-based reward shaping technique to provide more informative (dense) reward signals to the RL agent.
Ordinal Regression for Difficulty Estimation of StepMania Levels
As is common in popular games, there is a large number of community-designed levels.
