47 citations · 155 across the 17 of their papers we have counts for
17 papers
Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data
Mulugeta Weldezgina Asres, Christian Walter Omlin, The CMS-HCAL Collaboration
Extracting anomaly causality facilitates diagnostics once monitoring systems detect system faults. Identifying anomaly causes in large systems involves investigating a broader set…
Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection
Mulugeta Weldezgina Asres, Christian Walter Omlin, Long Wang +4
The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation i…
Lightweight Multi-System Multivariate Interconnection and Divergence Discovery
Mulugeta Weldezgina Asres, Christian Walter Omlin, Jay Dittmann +5
Identifying outlier behavior among sensors and subsystems is essential for discovering faults and facilitating diagnostics in large systems. At the same time, exploring large syste…
Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter
Mulugeta Weldezgina Asres, Christian Walter Omlin, Long Wang +12
The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality mon…
SECAdvisor: a Tool for Cybersecurity Planning using Economic Models
Muriel Figueredo Franco, Christian Omlin, Oliver Kamer +2
Cybersecurity planning is challenging for digitized companies that want adequate protection without overspending money. Currently, the lack of investments and perverse economic inc…
Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directions
Md. Haidar Sharif, Lei Jiao, Christian W. Omlin
Crowd anomaly detection is one of the most popular topics in computer vision in the context of smart cities. A plethora of deep learning methods have been proposed that generally o…
Symbolic Explanation of Affinity-Based Reinforcement Learning Agents with Markov Models
Charl Maree, Christian W. Omlin
The proliferation of artificial intelligence is increasingly dependent on model understanding. Understanding demands both an interpretation - a human reasoning about a model's beha…
Towards Artificial Virtuous Agents: Games, Dilemmas and Machine Learning
Ajay Vishwanath, Einar Duenger Bøhn, Ole-Christoffer Granmo +2
Machine ethics has received increasing attention over the past few years because of the need to ensure safe and reliable artificial intelligence (AI). The two dominantly used theor…
Balancing Profit, Risk, and Sustainability for Portfolio Management
Charl Maree, Christian W. Omlin
Stock portfolio optimization is the process of continuous reallocation of funds to a selection of stocks. This is a particularly well-suited problem for reinforcement learning, as…
Towards Responsible AI for Financial Transactions
Charl Maree, Jan Erik Modal, Christian W. Omlin
The application of AI in finance is increasingly dependent on the principles of responsible AI. These principles - explainability, fairness, privacy, accountability, transparency a…
Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management
Charl Maree, Christian W. Omlin
The common purpose of applying reinforcement learning (RL) to asset management is the maximization of profit. The extrinsic reward function used to learn an optimal strategy typica…
Can Interpretable Reinforcement Learning Manage Prosperity Your Way?
Charl Maree, Christian Omlin
Personalisation of products and services is fast becoming the driver of success in banking and commerce. Machine learning holds the promise of gaining a deeper understanding of and…
Reinforcement Learning Your Way: Agent Characterization through Policy Regularization
Charl Maree, Christian Omlin
The increased complexity of state-of-the-art reinforcement learning (RL) algorithms have resulted in an opacity that inhibits explainability and understanding. This has led to the…
Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities
Waddah Saeed, Christian Omlin
The past decade has seen significant progress in artificial intelligence (AI), which has resulted in algorithms being adopted for resolving a variety of problems. However, this suc…
Clustering in Recurrent Neural Networks for Micro-Segmentation using Spending Personality
Charl Maree, Christian W. Omlin
Customer segmentation has long been a productive field in banking. However, with new approaches to traditional problems come new opportunities. Fine-grained customer segments are n…
Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation
Charl Maree, Christian W. Omlin
Micro-segmentation of customers in the finance sector is a non-trivial task and has been an atypical omission from recent scientific literature. Where traditional segmentation clas…
The Convolutional Tsetlin Machine
Ole-Christoffer Granmo, Sondre Glimsdal, Lei Jiao +3
Convolutional neural networks (CNNs) have obtained astounding successes for important pattern recognition tasks, but they suffer from high computational complexity and the lack of…