Asset Management
7 papers with code • 0 benchmarks • 0 datasets
Asset management in ML is a discipline that offers engineers the necessary management support for processes and operations on different types of ML assets.
Benchmarks
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Libraries
Use these libraries to find Asset Management models and implementationsMost implemented papers
Semi-supervised Anomaly Detection using AutoEncoders
msminhas93/anomaly-detection-using-autoencoders
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Journal of Computational Vision and Imaging Systems 2020
But for defect detection lack of availability of a large number of anomalous instances and labelled data is a problem.
Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies
This is the first in a series of arti-cles dealing with machine learning in asset management.
Machine Learning in Asset Management—Part 2: Portfolio Construction—Weight Optimization. The Journal of Financial Data Science
This is the second in a series of articles dealing with machine learning in asset management.
Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask Learning
Utilising an interpretable hierarchical Bayesian approach and operational fleet data, domain expertise is naturally encoded (and appropriately shared) between different sub-groups, representing (i) use-type, (ii) component, or (iii) operating condition.
Reap the Harvest on Blockchain: A Survey of Yield Farming Protocols
Yield farming represents an immensely popular asset management activity in decentralized finance (DeFi).
SEBERTNets: Sequence Enhanced BERT Networks for Event Entity Extraction Tasks Oriented to the Finance Field
hecongqing/CCKS2019_EventEntityExtraction_Rank5
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In addition, motivated by recommendation system, we propose Hybrid Sequence Enhanced BERT Networks (HSEBERTNets for short), which uses a multi-channel recall method to recall all the corresponding event entity.
An Empirical Study of Challenges in Machine Learning Asset Management
In machine learning (ML), efficient asset management, including ML models, datasets, algorithms, and tools, is vital for resource optimization, consistent performance, and a streamlined development lifecycle.
