We also thank everyone who attended the workshop and submitted their papers. Check out all the accepted papers and details here: 📚👨‍🔬. #ML4CCE #ECML2024 #AI #MachineLearning #ChemicalEngineering #Innovation #Research #TechTalks.

Special thanks to our Keynote speakers: Venkat Venkatasubramanian (@ProfVenkat7), Dominik Grimm (@dg_grimm), and Felix Strieth-Kalthoff (@felix_s_k) for their incredible talks and insights! 🎤👏 Their contributions made the event truly memorable.

🚀 We had an incredible time hosting the ML4CCE Workshop at @ECMLPKDD 2024! 💡🔬 Experts from machine learning and chemical engineering came together to explore exciting topics like molecular design, process control, and anomaly detection, among others. 🎓✨

@Saurabh37965589 @purvanshi_mehta @waleedamustafa Authors: S. Varshneya (@SaurabVarshneya), A. Ledent, P. Liznerski, A. Balinskyy, P. Mehta (@purvanshi_mehta), W. Mustafa (@waleedamustafa), and M. Kloft.

Authors: .C. James, M. Nagda (@MayankNagda35), N. Ghassemi, M. Kloft, and S. Fellenz (@sophieImWeb2_0).

"Evaluating Dynamic Topic Models". 📝 We propose a novel evaluation measure for DTMs analyzing topic quality changes over time. This extension combines topic quality with temporal consistency, aiding in identifying changing topics and guiding future research.

🎉 Thrilled to announce that our paper is accepted at ACL 2024 @aclmeeting @aclanthology #acl2024 #NLProc:.

2. "Numerically Tight Generalization Bounds for Adversarial Risk in Stochastic Neural Networks" by W. Mustafa et al. 📝 Our novel generalization bounds predict model performance and robustness on unseen data in adversarial settings.

📝 In this paper, we dynamically adapt RL agents' behavior for moral decision-making by incorporating moral scores into the training objective. Findings highlight trade-offs between immoral behavior and performance.

1. "Ethics in Action: Training Reinforcement Learning Agent for Moral Decision-making In Text-based Adventure Games" by W. Li et al.

🎉 Excited to share our two papers that were presented at AISTATS 2024 (@aistats_conf):. #AISTATS2024 #AI #ML #RL.

📝 "Interpretable Tensor Fusion" introduces InTense, a multimodal learning method providing interpretability by disentangling data representations and their fusion.

2. "Interpretable Tensor Fusion". Authors: S. Varshneya, A. Ledent, P. Liznerski, A. Balinskyy, P. Mehta, W. Mustafa, and M. Kloft.

📝 "Putting Back the Stops" addresses the integration of syntax with neural topic models, enhancing interpretability and language understanding, and learning syntactic and semantic topics.

1. "Putting Back the Stops: Integrating Syntax with Neural Topic Models".Authors: Mayank Nagda (@MayankNagda35) and Sophie Fellenz (@sophieImWeb2_0 ).

🎉 Excited to announce our two papers accepted at IJCAI 2024 (@IJCAIconf):. The papers tackle crucial challenges in language understanding and multimodal learning. We will be presenting them at the conference. #IJCAI2024 #AI #NLP #MultimodalLearning.

checkout our full work: Authors: Phil Ostheimer (@PhilOstheimer), Mayank Nagda (@MayankNagda35), Marius Kloft, Sophie Fellenz (@sophieImWeb2_0).

📝 Text Style Transfer Evaluation Using Large Language Models. Presenting findings at #LREC-#COLING 2024. Our study explores LLMs' potential in TST assessment, revealing promising correlations with human evaluations. A new avenue for TST assessment emerges. #NLProc #AI

#ML #ECMLPKDD2024 @ECMLPKDD.

🎮Check out the paper "Ordinal Regression for Difficulty Estimation of StepMania Levels": �Explore the code here: 💻 and see a practical use case in action: 🚀 #MachineLearning #Gaming #AI #DDR.